Amazon Web Services DOP-C02 AWS Certified DevOps Engineer - Professional Exam Practice Test

Use cross-account EventBridge by configuring a rule in the source (automation) account to send events to the target accounts’ default event buses, and grant permissions on the target default event buses to accept events from the source account. This is the standard cross-account event routing model.

 Step 1: Automate Docker Image Deployment using AWS CodePipeline

The first challenge is the manual process of building and deploying Docker images. To address this, you can use AWS CodePipeline to automate the process. AWS CodePipeline integrates with CodeCommit (for source code and Dockerfile storage) and CodeBuild (to build Docker images and store them in Amazon Elastic Container Registry (ECR)).

Action: Create an AWS CodeCommit repository to store the Dockerfile and Kubernetes deployment files. Then, create a pipeline in AWS CodePipeline that triggers on new commits via an Amazon EventBridge event.

Why: This automation significantly reduces the manual effort of building and deploying Docker images when updates are made to the codebase.

To ensure that CloudTrail cannot be disabled in any member account, the control must be enforced above the individual accounts, at the organization level , and must not be overridable by local administrators. AWS Service Control Policies (SCPs) provide exactly this capability: they define permission guardrails that apply to all IAM users, roles, and even the root user in accounts within an AWS Organizations OU.

By applying an SCP that explicitly denies cloudtrail:StopLogging and cloudtrail:DeleteTrail , the organization prevents any principal in the affected accounts from disabling logging or deleting the trails, regardless of their IAM permissions. This provides strong, centralized enforcement of audit logging.

Option B uses IAM policies in each account. Local administrators with sufficient privileges could modify or detach those policies, defeating the control. Option C only sends notifications and does not prevent the action. Option D with AWS Config auto-remediation still allows a window where CloudTrail is disabled and also can be disabled or modified itself.

Therefore, the most robust and least bypassable approach is to use an SCP at the OU level to deny CloudTrail stop and delete actions.

EKS supports control plane logging sent directly to CloudWatch Logs.

CloudWatch Container Insights provides metrics and logs collection for nodes and containers automatically with minimal setup.

CloudWatch Logs Insights offers powerful querying and visualization integrated in AWS without managing additional infrastructure.

Options A and C require deploying and managing additional components (Logstash, Event Exporter, S3, Athena).

Option D adds complexity with OpenTelemetry and Redshift, which is more operationally heavy.

Configure a CloudWatch alarm on StatusCheckFailed_System and set the EC2 recovery action. Instance recovery automatically recovers the instance on impaired hardware while preserving the instance ID, private IPs, EBS volumes, and metadata.

AWS CodeDeploy natively supports automatic rollback based on CloudWatch alarms . To use this capability, you define an alarm that indicates unhealthy behavior (for example, increasing application error rate) and attach that alarm to the deployment group in CodeDeploy. If the alarm goes into ALARM state during or after a deployment, CodeDeploy automatically rolls back to the last known good revision when auto rollback is enabled.

Option A correctly implements this pattern:

Derive a CloudWatch metric from application logs (e.g., count of error-level log entries or HTTP 5xx responses).

Create a CloudWatch alarm on that metric with appropriate thresholds.

Configure the deployment group to use the alarm as part of its alarm configuration and enable auto rollback .

Option B uses CodeDeploy agent logs, which may not directly reflect true application health and couples rollback logic tightly to deployment internals rather than application behavior. Options C and D implement custom rollback logic with Lambda and EventBridge, which re-create behavior that CodeDeploy already provides natively and add unnecessary operational and code complexity.

Therefore, Option A is the simplest and most aligned with built-in CodeDeploy features for automated rollback based on application errors.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

For gradual traffic shifting in Lambda deployments, AWS Lambda aliases support the RoutingConfig property, which specifies the percentage of traffic routed to different versions of the Lambda function. This enables weighted traffic shifting between versions as part of deployment strategies.

The AWS::Lambda::Alias resource’s RoutingConfig can specify multiple versions with weights, enabling canary or linear deployment strategies without needing CodeDeploy resources explicitly.

AWS CodeDeploy resources like DeploymentConfig and DeploymentGroup are used primarily for blue/green deployments and managing deployment strategies outside Lambda aliases.

Lambda versions themselves do not have a VersionWeight property; instead, weighted routing is managed via aliases.

AWS recommends Application Auto Scaling for ECS services to dynamically adjust the number of running tasks based on Amazon SQS queue metrics such as ApproximateNumberOfMessagesVisible. By using target tracking scaling policies, ECS on Fargate scales automatically when the queue backlog grows and scales down when traffic decreases — a fully managed, cost-efficient solution (see ECS Service Auto Scaling Developer Guide).

AWS CodeDeploy supports deployment of both Lambda functions and ECS services with native integration.

It provides deployment strategies such as canary deployments for Lambda and blue/green deployments for ECS, which minimize downtime and risk.

CodeDeploy automates the deployment process, which reduces manual effort and improves consistency.

AWS Systems Manager Parameter Store is a simple, managed solution for storing configuration data securely and at scale.

Option A uses CloudFormation stack updates with resource replacement, which can cause downtime and is less flexible for minimizing downtime compared to CodeDeploy’s deployment strategies.

Option C adds unnecessary orchestration complexity with Step Functions and cross-region deployments, increasing deployment effort.

Option D’s all-at-once deployments for Lambda and rolling updates for ECS do not minimize downtime as effectively as canary and blue/green strategies. Hence, option B meets the requirement with the least deployment effort and maximizes automation and availability.

Reference from AWS Official Documentation:

Deploying Lambda Functions with CodeDeploy: " AWS CodeDeploy supports canary and linear deployment strategies for AWS Lambda functions to reduce deployment risk. " (CodeDeploy Lambda Deployment)

Deploying ECS Services with CodeDeploy Blue/Green: " CodeDeploy supports blue/green deployments for ECS to minimize downtime during service updates. " (CodeDeploy ECS Blue/Green)

AWS Systems Manager Parameter Store: " Parameter Store provides secure, hierarchical storage for configuration data management. " (Systems Manager Parameter Store)

https://docs.aws.amazon.com/codecommit/latest/userguide/how-to-amazon-codeguru-reviewer.html

In CodePipeline’s execution mode,

PARALLEL mode for pull_request pipelines ensures that multiple feature branches can be tested simultaneously for quick feedback.

QUEUED mode for main_branch ensures deployments run sequentially — each must finish before the next begins, preventing overlap. This configuration aligns with AWS CodePipeline best practices for trunk-based development and concurrent test pipelines.

The following are the steps that the DevOps engineer should take to ensure that all EBS volumes always have the Backup_Frequency tag so that the company can perform backups at least weekly unless a different value is specified:

Set up AWS Config in the account.

Use a managed rule that returns a compliance failure for EC2::Volume resources that do not have a Backup Frequency tag applied.

Configure a remediation action that uses a custom AWS Systems Manager Automation runbook to apply the Backup_Frequency tag with a value of weekly.

The managed rule AWS::Config::EBSVolumesWithoutBackupTag will return a compliance failure for any EBS volume that does not have the Backup_Frequency tag applied. The remediation action will then use the Systems Manager Automation runbook to apply the Backup_Frequency tag with a value of weekly to the EBS volume.

https://aws.amazon.com/blogs/mt/aws-config-auto-remediation-s3-compliance/ https://aws.amazon.com/blogs/aws/aws-config-rules-dynamic-compliance-checking-for-cloud-resources/

Option A is the most correct because it provides both : (1) automated patching and (2) compliance monitoring/enforcement across a fleet, using AWS-native services built for exactly this purpose.

AWS Systems Manager Patch Manager is designed to automate patching of managed instances using patch baselines , maintenance windows (or on-demand), and it produces compliance status for patching. It’s the standard AWS service to apply OS/security patches at scale without SSH’ing into instances.

AWS Config can be used to evaluate and track compliance over time against defined rules, giving centralized visibility and continuous compliance assessment. With remediation , Config can invoke Systems Manager Automation documents to correct non-compliant resources or trigger patch actions (depending on the rule/remediation design). This meets the “monitor and enforce” requirement.

Why the other options don’t meet requirements as well:

B is manual, doesn’t scale well, and increases operational risk (key management, human error). It’s not “automated monitoring and enforcement.”

C (replacing instances with new AMIs) can be part of an immutable infrastructure strategy, but by itself it does not provide compliance monitoring across the current fleet, and scaling policies based on CloudWatch metrics are unrelated to patch compliance. Also, patch cadence would depend on AMI pipelines and instance rotation rather than direct compliance enforcement.

D is operationally heavy and mismatched: CloudTrail records API activity; it does not natively provide “patch compliance” status for instance OS packages. Recreating instances via CloudFormation for every patch is not an efficient or standard enforcement mechanism for patch compliance.

The key requirement is to shift a small percentage of traffic to a new version first to validate changes before rolling out to the entire ECS service, thereby reducing downtime and deployment risk. For ECS services behind an Application Load Balancer, the AWS-recommended and most reliable approach is to use AWS CodeDeploy with blue/green or canary deployments .

Option B directly addresses this requirement. By configuring the ECS service to use CodeDeploy as the deployment controller , CodeDeploy can manage traffic shifting between two ALB target groups: one for the current (stable) version and one for the new version. The CodeDeployDefault.ECSCanary10Percent15Minutes deployment configuration initially routes 10% of production traffic to the new task set, monitors the deployment for issues, and then shifts the remaining traffic after the defined interval. This enables early detection of application errors while limiting user impact.

Option A and C rely on rolling updates , which replace tasks incrementally but do not provide precise traffic control or automated rollback based on health checks. Option D’s slow start setting only affects how quickly targets receive traffic after registration and does not implement true canary testing.

Therefore, using AWS CodeDeploy with ECS canary deployments is the most robust, AWS-supported solution to test changes safely before full rollout.

 For deploying a Ruby-based application with requirements for interaction with an Amazon RDS for MySQL database, automatic scaling, high availability, and data persistence, the following steps will meet the requirements:

B. Deploy the application on AWS Elastic Beanstalk. Deploy a separate Amazon RDS for MySQL DB instance outside of Elastic Beanstalk. This approach ensures that the database persists independently of the Elastic Beanstalk environment, which can be torn down and recreated without affecting the database123.

E. Use the immutable deployment method to deploy new application versions. Immutable deployments provide a zero-downtime deployment method that ensures that if any part of the deployment process fails, the environment is rolled back to the original state automatically4.

D. Configure an Amazon EventBridge rule to monitor AWS Health events. Use an Amazon Simple Notification Service (Amazon SNS) topic as a target to alert the application team. This setup allows for automated monitoring and alerting of the application team in case of deployment failures or other health events56.

AWS Elastic Beanstalk documentation on deploying Ruby applications1.

AWS documentation on application auto-scaling7.

AWS documentation on automated deployment strategies with automatic rollbacks and alerts456.

The correct answer is B. Creating a new AWS CodeBuild project and configuring a test stage in the AWS CodePipeline pipeline that uses the new CodeBuild project is the best way to integrate the unit tests into the existing pipeline. Creating a CodeBuild report group and uploading the test reports to the new CodeBuild report group will produce reports about the unit tests for the company to view. Using JUNITXML as the output format for the unit tests is supported by CodeBuild and will generate a valid report.

Option A is incorrect because Amazon CodeGuru Reviewer is a service that provides automated code reviews and recommendations for improving code quality and performance. It is not a tool for running unit tests or producing test reports. Therefore, option A will not meet the requirements.

Option C is incorrect because AWS CodeArtifact is a service that provides secure, scalable, and cost-effective artifact management for software development. It is not a tool for running unit tests or producing test reports. Moreover, option C uses CUCUMBERJSON as the output format for the unit tests, which is not supported by CodeBuild and will not generate a valid report.

Option D is incorrect because uploading the test reports to an Amazon S3 bucket is not the best way to produce reports about the unit tests for the company to view. CodeBuild has a built-in feature to create and manage test reports, which is more convenient and efficient than using S3. Furthermore, option D uses HTML as the output format for the unit tests, which is not supported by CodeBuild and will not generate a valid report.

ECR Lifecycle Policies automatically manage image retention based on tag status, age, or count. They execute natively within the ECR service, requiring no external management or scripts — the least overhead and AWS-recommended cleanup method.

The requirement is to update the infrastructure to ensure that only the Lambda function’s execution role can access the values in Secrets Manager. The solution must apply the principle of least privilege, which means granting the minimum permissions necessary to perform a task.

To do this, the DevOps engineer needs to use the following steps:

Create a KMS customer managed key that trusts Secrets Manager and allows the Lambda function’s execution role to decrypt. A customer managed key is a symmetric encryption key that is fully managed by the customer. The customer can define the key policy, which specifies who can use and manage the key. By creating a customer managed key, the DevOps engineer can restrict the decryption permission to only the Lambda function’s execution role, and prevent other principals from accessing the secret values. The customer managed key also needs to trust Secrets Manager, which means allowing Secrets Manager to use the key to encrypt and decrypt secrets on behalf of the customer.

Update Secrets Manager to use the new customer managed key. Secrets Manager allows customers to choose which KMS key to use for encrypting each secret. By default, Secrets Manager uses the default KMS key for Secrets Manager, which is a service-managed key that is shared by all customers in the same AWS Region. By updating Secrets Manager to use the new customer managed key, the DevOps engineer can ensure that only the Lambda function’s execution role can decrypt the secret values using that key.

Ensure that the Lambda function’s execution role has the KMS permissions scoped on the resource level. The Lambda function’s execution role is an IAM role that grants permissions to the Lambda function to access AWS services and resources. The role needs to have KMS permissions to use the customer managed key for decryption. However, to apply the principle of least privilege, the role should have the permissions scoped on the resource level, which means specifying the ARN of the customer managed key as a condition in the IAM policy statement. This way, the role can only use that specific key and not any other KMS keys in the account.

If instead you want to automatically apply the policy to existing in-scope resources, choose Auto remediate any noncompliant resources. This option creates a web ACL in each applicable account within the AWS organization and associates the web ACL with the resources in the accounts. When you choose Auto remediate any noncompliant resources, you can also choose to remove existing web ACL associations from in-scope resources, for the web ACLs that aren ' t managed by another active Firewall Manager policy. If you choose this option, Firewall Manager first associates the policy ' s web ACL with the resources, and then removes the prior associations. If a resource has an association with another web ACL that ' s managed by a different active Firewall Manager policy, this choice doesn ' t affect that association.

To meet the requirements of ensuring that flow logs remain configured for all existing and new VPCs in the AWS account, the company should use AWS Config and automatic remediation. AWS Config is a service that enables customers to assess, audit, and evaluate the configurations of their AWS resources. AWS Config continuously monitors and records the configuration changes of the AWS resources and evaluates them against desired configurations. Customers can use AWS Config rules to define the desired configuration state of their AWS resources and trigger actions when a resource configuration violates a rule.

One of the AWS Config rules that customers can use is vpc-flow-logs-enabled, which checks whether VPC flow logs are enabled for all VPCs in an AWS account. Customers can also configure automatic remediation for this rule, which means that AWS Config will automatically enable VPC flow logs for any VPCs that do not have them enabled. Customers can specify the destination (CloudWatch Logs or S3) and the traffic type (all, accept, or reject) for the flow logs as remediation parameters. By using AWS Config and automatic remediation, the company can ensure that flow logs remain configured for all existing and new VPCs in its AWS account, regardless of who creates them or how they are created.

The other options are not correct because they do not meet the requirements or follow best practices. Adding the resource to the CloudFormation stack that creates the VPCs is not a sufficient solution because it will only work for VPCs that are created by using the CloudFormation stack. It will not work for VPCs that are created by using other methods, such as the console or the API. Creating an organization in AWS Organizations and creating an SCP to prevent users from modifying VPC flow logs is not a good solution because it will not ensure that flow logs are enabled for all VPCs in the first place. It will only prevent users from disabling or changing flow logs after they are enabled. Creating an IAM policy to deny the use of API calls for VPC flow logs and attaching it to all IAM users is not a valid solution because it will prevent users from enabling or disabling flow logs at all. It will also not work for VPCs that are created by using other methods, such as the console or CloudFormation.

1: AWS::EC2::FlowLog - AWS CloudFormation

2: Amazon VPC Flow Logs extends CloudFormation Support to custom format subscriptions, 1-minute aggregation intervals and tagging

3: Logging IP traffic using VPC Flow Logs - Amazon Virtual Private Cloud

About AWS Config - AWS Config

vpc-flow-logs-enabled - AWS Config

Remediate Noncompliant Resources with AWS Config Rules - AWS Config

The correct answer is C. Allowing users to deploy CloudFormation stacks using AWS Service Catalog only and enforcing the use of a launch constraint is the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. AWS Service Catalog is a service that enables organizations to create and manage catalogs of IT services that are approved for use on AWS. A launch constraint is a rule that specifies the role that AWS Service Catalog assumes when launching a product. By using a launch constraint, the DevOps engineer can control the permissions that the users have when launching a product. Using AWS Config rules to detect when resources have drifted from their expected state is the best way to automate the monitoring of the resources. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. AWS Config rules are custom or managed rules that AWS Config uses to evaluate whether your AWS resources comply with your desired configurations. By using AWS Config rules, the DevOps engineer can track the changes in the resources and identify any non-compliant resources.

Option A is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A CloudFormation service role is an IAM role that CloudFormation assumes to create, update, or delete the stack resources. By using a CloudFormation service role, the DevOps engineer can control the permissions that CloudFormation has when acting on the resources, but not the permissions that the users have when launching a stack. Therefore, option A does not prevent the users from launching resources that are not approved by the company. Using CloudFormation drift detection to detect when resources have drifted from their expected state is a valid way to monitor the resources, but it is not as automated and scalable as using AWS Config rules. CloudFormation drift detection is a feature that enables you to detect whether a stack’s actual configuration differs, or has drifted, from its expected configuration. To use this feature, the DevOps engineer would need to manually initiate a drift detection operation on the stack or the stack resources, and then view the drift status and details in the CloudFormation console or API.

Option B is incorrect because allowing users to deploy CloudFormation stacks using a CloudFormation service role only is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only, as explained in option A. Using AWS Config rules to detect when resources have drifted from their expected state is a valid way to monitor the resources, as explained in option C.

Option D is incorrect because enforcing the use of a template constraint is not the best way to ensure that the internal business teams launch resources through pre-approved CloudFormation templates only. A template constraint is a rule that defines the values or properties that users can specify when launching a product. By using a template constraint, the DevOps engineer can control the parameters that the users can provide when launching a product, but not the permissions that the users have when launching a product. Therefore, option D does not prevent the users from launching resources that are not approved by the company. Using Amazon EventBridge notifications to detect when resources have drifted from their expected state is a less reliable and consistent solution than using AWS Config rules. Amazon EventBridge is a service that enables you to connect your applications with data from a variety of sources. Amazon EventBridge can deliver a stream of real-time data from event sources, such as AWS services, and route that data to targets, such as AWS Lambda functions. However, to use this solution, the DevOps engineer would need to configure the event source, the event bus, the event rule, and the event target for each resource type that needs to be monitored, which is more complex and error-prone than using AWS Config rules.

To prevent ApplicationA from being installed on the additional instance, the deployment group configuration needs to be more specific. By changing the current single tag group to include only the Environment=Production tag and adding another single tag group that includes only the Name=ApplicationA tag, the deployment process will target only the instances that match both tag groups. This ensures that only instances intended for ApplicationA with the correct environment and name tags will receive the deployment, thus excluding the additional instance with the Department=Marketing and Name=ApplicationB tags.

AWS CodeDeploy Documentation: Working with instances for CodeDeploy

AWS CodeDeploy Documentation: Stop a deployment with CodeDeploy

Stack Overflow Discussion: CodeDeploy Deployment failed to stop Application

RDS Multi-AZ cluster deployments with cross-Region read replicas support near real-time replication ( < 1 min RPO) and fast promotion ( < 10 min RTO). Route 53 CNAME failover redirects application traffic automatically. This is the AWS-recommended DR pattern.

The correct answer is B and E. Configuring a higher provisioned concurrency for the Lambda functions will ensure that the functions are ready to respond to the initial burst of requests without any cold start latency. Using Amazon RDS Proxy to create a proxy for the Aurora database will enable the Lambda functions to reuse existing database connections and reduce the overhead of establishing new ones. This will also improve the scalability and availability of the database by managing the connection pool size and handling failovers. Option A is incorrect because reserved concurrency only limits the number of concurrent executions for a function, not pre-warms them. Option C is incorrect because converting the DB cluster to an Aurora global database will not address the issue of database connection latency, and may introduce additional costs and complexity. Option D is incorrect because moving the code blocks that initialize database connections into the function handlers will not improve the performance or scalability of the Lambda functions, and may actually worsen the cold start latency. References:

AWS Lambda Provisioned Concurrency

Using Amazon RDS Proxy with AWS Lambda

Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 173)

Comprehensive and Detailed Explanation From Exact Extract of DevOps Engineer documents only:

During an Amazon ECS service deployment, if tasks fail the Elastic Load Balancing target group health checks, Amazon ECS considers those tasks unhealthy and stops and replaces them. This behavior can cause the deployment to remain incomplete because healthy replacement tasks never become available.

Also, if an essential container in the task exits, the entire task stops. Since the question states that the new tasks enter a stopped state soon after launch, an essential container failure is a direct and highly likely cause.

Why the other options are incorrect:

B. The IAM role used by the DevOps engineer to update the ECS service does not need RunTask permission for the tasks to remain running after launch. A permission issue here would more likely affect the update action itself rather than cause launched tasks to stop shortly afterward.

C. Although CloudWatch Logs configuration problems can affect logging, the missing log group is not the most likely direct reason for ECS tasks to repeatedly stop in this deployment scenario.

D. The task role provides permissions to the application code running inside the container. It is not the role primarily responsible for launching the task. Task startup is typically related to the task execution role, ECS service behavior, container health, and load balancer checks.

The following are the steps involved in accomplishing this in the most maintainable manner:

Use AWS CodeBuild with artifact encryption to replace the Jenkins instance running on EC2 instances.

Configure CodeBuild to encrypt the build artifacts using AWS Secrets Manager.

Deploy the containerized quality control applications to CodeBuild.

This approach is the most maintainable because it eliminates the need to manage Jenkins on EC2 instances. CodeBuild is a managed service, so the DevOps engineer does not need to worry about patching or upgrading the service.

https://docs.aws.amazon.com/codebuild/latest/userguide/security-encryption.html Build artifact encryption - CodeBuild requires access to an AWS KMS CMK in order to encrypt its build output artifacts. By default, CodeBuild uses an AWS Key Management Service CMK for Amazon S3 in your AWS account. If you do not want to use this CMK, you must create and configure a customer-managed CMK. For more information Creating keys.

 The UserData property of the AWS: AutoScaling: LaunchConfiguration resource can be used to specify a script that runs when the EC2 instances are launched. This script can include the ECS cluster name as an environment variable for the ECS agent running on the EC2 instances. This way, the EC2 instances will register with the correct ECS cluster. Option A is incorrect because the AWS: ECS: Cluster resource does not have a property to reference the EC2 instances. Option C is incorrect because the EC2 instances are launched by the Auto Scaling group, not by the AWS: EC2: Instance resource. Option D is incorrect because using a custom resource and a Lambda function is unnecessary and overly complex for this scenario. References: AWS::AutoScaling::LaunchConfiguration, Amazon ECS Container Agent Configuration

Step 1: Handling Invalid Data with Failed Batch ItemsThe Lambda function is processing batches of messages, and some messages contain invalid data, causing processing delays. Lambda provides the capability to report failed batch items, which allows valid messages to be processed while skipping invalid ones. This functionality ensures that the valid messages are processed within the required timeline.

Action: Keep the Lambda function ' s batch size the same and configure it to report failed batch items.

Why: By reporting failed batch items, the Lambda function can skip invalid messages and continue processing valid ones, ensuring that they meet the processing timeline.

Step 1: Enabling AWS Config for the OrganizationThe first step is to enable AWS Config across the AWS Organization. AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. By enabling AWS Config, you can ensure that all S3 buckets within the organization are tracked and evaluated according to compliance rules.

Action: Turn on AWS Config for all AWS accounts in the organization.

Why: AWS Config will help monitor all resources (like S3 buckets) in real time to detect whether they are compliant with security policies.

AWS recommends centralizing CodeDeploy agent logs in Amazon CloudWatch Logs for efficient analysis. By configuring the CloudWatch agent or using the CodeDeploy console integration, you can stream logs in near-real time and query them with CloudWatch Logs Insights. This enables filtering by deployment lifecycle events, saving time versus manually connecting to instances. Refer to AWS documentation section “Monitoring and troubleshooting deployments with CodeDeploy and CloudWatch.”

https://docs.aws.amazon.com/systems-manager/latest/userguide/patch-manager-how-it-works-alt-source-repository.html

Step 1: Reacting to RDS Storage Autoscaling Events Using Amazon EventBridgeAmazon RDS emits events when storage autoscaling occurs. To visualize these events in a CloudWatch dashboard, you can create an EventBridge rule that listens for these specific autoscaling events.

Action: Create an EventBridge rule that reacts to RDS storage autoscaling events from the RDS event stream.

Why: EventBridge allows you to listen to RDS events and route them to specific AWS services for processing.

Step 2: Creating a Custom CloudWatch Metric via LambdaOnce the EventBridge rule detects a storage autoscaling event, you can use a Lambda function to publish a custom metric to CloudWatch. This metric can then be visualized in a CloudWatch dashboard.

Action: Use a Lambda function to publish custom metrics to CloudWatch based on the RDS storage autoscaling events.

Why: Custom metrics allow you to track specific events like autoscaling and visualize them easily on a CloudWatch dashboard.

The correct answer is D. Creating an AWS Config conformance pack that contains the AWS Config rules and remediation actions and deploying it from the delegated administrator account by using AWS Config will meet the requirements. A conformance pack is a collection of AWS Config rules and remediation actions that can be easily deployed as a single entity in an account and a region or across an organization in AWS Organizations1. By using the delegated administrator account, the DevOps engineer can centrally manage the conformance pack and prevent non-administrator users from modifying it in the member accounts. Option A is incorrect because creating a CloudFormation template that contains the AWS Config rules and remediation actions and deploying it from the Organizations management account by using CloudFormation StackSets will not prevent non-administrator users from modifying the AWS Config rules in the member accounts. Option B is incorrect because deploying the conformance pack from the Organizations management account by using CloudFormation StackSets will not use the trusted access feature of AWS Config and will require additional permissions and resources. Option C is incorrect because creating a CloudFormation template that contains the AWS Config rules and remediation actions and deploying it from the delegated administrator account by using AWS Config will not leverage the benefits of conformance packs, such as simplified deployment and management. References:

Conformance Packs - AWS Config

Certified DevOps Engineer - Professional (DOP-C02) Study Guide (page 176)

https://docs.aws.amazon.com/vpc/latest/userguide/managed-prefix-lists.html A managed prefix list is a set of one or more CIDR blocks. You can use prefix lists to make it easier to configure and maintain your security groups and route tables. https://docs.aws.amazon.com/vpc/latest/userguide/sharing-managed-prefix-lists.html With AWS Resource Access Manager (AWS RAM), the owner of a prefix list can share a prefix list with the following: Specific AWS accounts inside or outside of its organization in AWS Organizations An organizational unit inside its organization in AWS Organizations An entire organization in AWS Organizations

You can run custom recipes manually, but the best approach is usually to have AWS OpsWorks Stacks run them automatically. Every layer has a set of built-in recipes assigned each of five lifecycle events—Setup, Configure, Deploy, Undeploy, and Shutdown. Each time an event occurs for an instance, AWS OpsWorks Stacks runs the associated recipes for each of the instance ' s layers, which handle the corresponding tasks. For example, when an instance finishes booting, AWS OpsWorks Stacks triggers a Setup event. This event runs the associated layer ' s Setup recipes, which typically handle tasks such as installing and configuring packages

CodePipeline V2 with QUEUED mode ensures that new executions wait for the previous execution to finish, preventing overlapping runs.

Adding a trigger filter to listen for Git tags triggers the pipeline only on new tag pushes.

An EventBridge rule can detect new image pushes to ECR and start the deployment pipeline, integrating the build and deploy pipelines effectively.

SUPERSEDED mode cancels the previous run when a new one starts, which is not desired here.

Using branches instead of tags would trigger on all commits, not just releases.

Step 1: Reducing Replication Lag in Aurora Global DatabasesIn Amazon Aurora global databases, write operations on the primary cluster can be delayed due to the time it takes to replicate to secondary clusters, especially when there are multiple secondary regions involved.

Issue: The write operations are occasionally blocked due to the RPO setting, which guarantees replication within 60 seconds.

Action: Remove one of the secondary clusters from the global database.

Why: Fewer secondary clusters will reduce the overall replication lag, improving write performance and reducing the frequency of blocked writes.

Installing the CloudWatch agent and instrumenting the application to push custom metrics to the agent is the easiest and lowest overhead method.

Prometheus (B) adds operational complexity.

Lambda polling (C) introduces unnecessary complexity and latency.

Using Logs Insights (D) requires extracting metrics from logs, which is less efficient.

To meet the requirements, the DevOps engineer should do the following:

Turn on the Multi-AZ option on the Aurora cluster.

Update the application to use the Aurora cluster endpoint for write operations.

Update the Aurora cluster ' s reader endpoint for reads.

Turning on the Multi-AZ option will create a replica of the database in a different Availability Zone. This will ensure that the database remains available even if one of the Availability Zones is unavailable.

Updating the application to use the Aurora cluster endpoint for write operations will ensure that all writes are sent to both the primary and replica databases. This will ensure that the data is always consistent.

Updating the Aurora cluster ' s reader endpoint for reads will allow the application to read data from the replica database. This will improve the performance of the application during the maintenance window.

" You can use subscriptions to get access to a real-time feed of log events from CloudWatch Logs and have it delivered to other services such as an Amazon Kinesis stream, an Amazon Kinesis Data Firehose stream, or AWS Lambda for custom processing, analysis, or loading to other systems. When log events are sent to the receiving service, they are Base64 encoded and compressed with the gzip format. " See https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/Subscriptions.html

 Step 1: Centralizing Image Scanning in a Shared Services Account

The first requirement is to centralize the image scanning process, ensuring pre-scan and post-scan images are stored separately. This can be achieved by creating separate pre-scan and post-scan repositories in the shared services account, with the appropriate resource-based policies to control access.

Action: Create separate ECR repositories for pre-scan and post-scan images in the shared services account. Configure resource-based policies to allow write access to pre-scan repositories and read access to post-scan repositories.

Why: This ensures that images are isolated before and after the scan, following the compliance requirements.

To meet the requirements of failover and disaster recovery, the company should use the following deployment strategies:

Create an Amazon Aurora global database in two AWS Regions as the data store. In the event of a failure, promote the secondary Region to the primary for the application. Update the application to use the Aurora cluster endpoint in the secondary Region. This strategy can provide a low RPO and RTO for the data, as Aurora global database replicates data with minimal latency across Regions and allows fast and easy failover12. The company can use the Amazon Aurora cluster endpoint to connect to the current primary DB cluster without needing to change any application code1.

Set up the application in two AWS Regions. Configure AWS Global Accelerator to point to Application Load Balancers (ALBs) in both Regions. Add both ALBs to a single endpoint group. Use health checks and Auto Scaling groups in each Region. This strategy can provide high availability and performance for the application, as AWS Global Accelerator uses the AWS global network to route traffic to the closest healthy endpoint3. The company can also use static IP addresses that are assigned by Global Accelerator as a fixed entry point for their application1. By using health checks and Auto Scaling groups, the company can ensure that their application can scale up or down based on demand and handle any instance failures4.

The other options are incorrect because:

Creating an Amazon Aurora Single-AZ cluster in multiple AWS Regions as the data store would not provide a fast failover or disaster recovery solution, as the company would need to manually restore data from backups or snapshots in another Region in case of a failure.

Creating an Amazon Aurora cluster in multiple AWS Regions as the data store and using a Network Load Balancer to balance the database traffic in different Regions would not work, as Network Load Balancers do not support cross-Region routing. Moreover, this strategy would not provide a consistent view of the data across Regions, as Aurora clusters do not replicate data automatically between Regions unless they are part of a global database.

Setting up the application in two AWS Regions and using Amazon Route 53 failover routing that points to Application Load Balancers in both Regions would not provide a low RTO, as Route 53 failover routing relies on DNS resolution, which can take time to propagate changes across different DNS servers and clients. Moreover, this strategy would not provide deterministic routing, as Route 53 failover routing depends on DNS caching behavior, which can vary depending on different factors.

This solution will meet the requirements because it will use a rolling restart to gradually replace the EC2 instances in the green environment with new instances that have the new software version installed. A rolling restart is a process that terminates and launches instances in batches, ensuring that there is always a minimum number of healthy instances in service. This way, the green environment can be updated without affecting the availability or performance of the application. When the rolling restart is complete, the DevOps engineer can use an AWS CLI command to modify the listener rules of the ALB and change the default action to forward traffic to the green environment’s target group. This will switch the traffic from the blue environment to the green environment all at once, as required by the question.

To allow developers to create IAM users without granting excessive permissions, the correct solution is to use permissions boundaries, which AWS specifically recommends for restricting delegated administrators such as developers. A permissions boundary defines the maximum permissions that an IAM user or role can delegate.

Step C ensures that each AWS account contains a PermissionBoundaries policy defining the maximum allowed permissions that any developer-created user may receive. This prevents privilege escalation, even if the developer attaches a more powerful policy. This aligns with AWS guidance for restricting privilege escalation within multi-account environments.

Step E ensures that developers can create IAM users but only if they attach the PermissionBoundaries policy as the permissions boundary. By attaching the DeveloperBoundary policy to the CreateAndManageUsers role, developers gain the ability to create users, but they are cryptographically prevented from assigning permissions outside the boundary policy.

Meanwhile, the DevOps team (who are not restricted by the boundary) can still create IAM users with full permissions.

This combination satisfies all constraints:

DevOps team: unrestricted IAM creation

Developers: restricted IAM creation enforced by boundaries

Other users: still blocked from IAM creation by existing SCP

The root cause of the problem is that the RDS PostgreSQL instance is exceeding its database connection limit due to high volumes of concurrent GET requests. The most efficient and lowest-effort remediation is to reduce the number of connections that reach the database and serve as many reads as possible from a cache layer. Option B accomplishes both goals with minimal changes to the existing architecture.

First, RDS Proxy manages database connections efficiently by pooling and reusing them. This reduces connection churn and prevents the Lambda function from opening excessive concurrent connections during traffic spikes. RDS Proxy is natively integrated with RDS and requires only minor configuration changes without altering application logic.

Second, enabling API Gateway caching allows caching GET responses directly at the API layer. Because over 90% of requests are GET operations, enabling caching drastically reduces traffic hitting both Lambda and RDS, significantly improving performance and lowering connection pressure.

Option C also improves read scaling, but adding ElastiCache introduces additional infrastructure and requires modifying application code to query Redis before querying the database. This is more complex than enabling API Gateway caching. Options A and D require full database migration, which is far more labor-intensive.

Option B is the simplest, most effective, and most aligned with AWS best practices for reducing RDS connection saturation.

Step A: Using a tool like CloudFormation resource import or IaC generator to scan and create a template from existing resources is efficient to bring current infrastructure under management.

Step C: Using CodeConnections (AWS ' s solution to connect Git repositories) with AWS CodePipeline ensures any changes to CloudFormation templates in the Git repo automatically deploy infrastructure changes, enforcing infrastructure as code workflows.

Step E: Creating an IAM role with CloudFormation as the principal ensures CloudFormation has permissions to manage resources. Using an SCP to deny all actions except by this role enforces strict control, preventing manual changes outside the pipeline. Option B uses AWS Config which is more for compliance and auditing, not direct resource import. Option D is invalid because CloudFormation does not natively sync with Git; CodePipeline does. Option F is less secure than denying all except the IAM role.

To restore the functionality of the Auto Scaling group after the AMI was deleted, the DevOps engineer needs to create a new AMI and update the Auto Scaling group to use it. The DevOps engineer can create a new AMI by copying the most recent public AMI of the operating system that the EC2 instances use. This will ensure that the new AMI has the same operating system as the custom AMI that was deleted. The DevOps engineer can then create a new launch template that uses the new AMI and update the Auto Scaling group to use the new launch template. This will allow the Auto Scaling group to launch new instances with the new AMI.

https://aws.amazon.com/blogs/devops/how-to-test-and-debug-aws-codedeploy-locally-before-you-ship-your-code/#:~:text=You%20can%20test%20application%20code,local%20server%20or%20EC2%20instance.

AWS CloudWatch Events can be used to monitor events across different AWS resources, and a CloudWatch Event Rule can be created to trigger an AWS Lambda function when a deployment issue is detected in the pipeline. The Lambda function can then evaluate the issue and send a notification to the appropriate stakeholders through an Amazon SNS topic. This approach allows for real-time notifications and faster resolution times.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/install-CloudWatch-Agent-on-premise.html https://docs.aws.amazon.com/xray/latest/devguide/xray-api-sendingdata.html

Amazon FSx for NetApp ONTAP provides NetApp ONTAP features in AWS, including SnapMirror replication and storage efficiencies like deduplication and compression. Create FSx for ONTAP in each Region and use SnapMirror from on-prem ONTAP to each Region for efficient, incremental replication. Regions can serve data independently of on-prem availability once replicated.

Container restart failures require detailed observability into pod-level, node-level, and container-level metrics and logs. CloudWatch Container Insights is purpose-built for Kubernetes operational diagnostics and provides granular visibility into CPU, memory, network I/O, disk I/O, container restarts, OOM kills, throttling, pod lifecycle issues, and Kubernetes control plane behaviors.

The CloudWatch Observability add-on deploys Fluent Bit and the CloudWatch Agent directly into the EKS cluster as DaemonSets. These components automatically collect:

Container logs

Pod metrics

Node metrics

Cluster events

OOM errors

CrashLoopBackOff restart cycles

Control plane request anomalies

With this data, the DevOps engineer can easily identify misconfigurations, resource bottlenecks, unhealthy nodes, failing containers, or image pull issues.

Option A (dashboards only) lacks per-container diagnostic data.

Option B (CloudTrail) only logs API calls — not useful for restart debugging.

Option C (CloudTrail Insights) only detects anomalous API usage, not container failures.

Therefore, CloudWatch Container Insights is the correct and AWS-recommended solution for diagnosing container restart failures in EKS.

Option A is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring automatic rollbacks on the deployment group is not operationally efficient, as it requires manual intervention to fix the errors and redeploy the application.

Option B is correct because setting the deployment configuration to LambdaCanary10Percent10Minutes means that the new version of the application will be deployed to 10 percent of the Lambda functions first, and then to the remaining 90 percent after 10 minutes. This minimizes the impact of errors on customers, as only 10 percent of them will be affected by a faulty deployment. Configuring automatic rollbacks on the deployment group also meets the requirement of reverting to the most recent stable version of the application when an error is detected. Creating a CloudWatch alarm that detects HTTP Bad Gateway errors on API Gateway is a valid way to monitor the health of the application and trigger a rollback if needed.

Option C is incorrect because setting the deployment configuration to LambdaAllAtOnce means that the new version of the application will be deployed to all Lambda functions at once, affecting all customers. This does not meet the requirement of affecting the fewest customers possible. Moreover, configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating an SNS topic to send notifications every time a deployment fails is not sufficient to detect errors in the application, as it does not monitor the API Gateway responses.

Option D is incorrect because configuring manual rollbacks on the deployment group is not operationally efficient, as it requires human intervention to stop the current deployment and start a new one. Creating a metric filter on a CloudWatch log group for API Gateway to monitor HTTP Bad Gateway errors is a valid way to monitor the health of the application, but invoking a new Lambda function to perform a rollback is unnecessary and complex, as CodeDeploy already provides automatic rollback functionality.

 Ensure that Amazon GuardDuty is Enabled:

Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior.

It can detect port scans and generate findings for these events.

 Create an Amazon CloudWatch Alarm for Detected EC2 and Port Scan Findings:

Configure GuardDuty to monitor for port scans and other threats.

Create a CloudWatch alarm that triggers when GuardDuty detects port scan activities.

 Connect the Alarm to the SNS Topic:

The CloudWatch alarm should be configured to send notifications to the SNS topic subscribed by the security team.

This setup ensures that the security team receives near-real-time notifications when a port scan is detected on the EC2 instances.

Example configuration steps:

Enable GuardDuty and ensure it is monitoring the relevant AWS accounts.

Create a CloudWatch alarm:

{

" AlarmName " : " GuardDutyPortScanAlarm " ,

" MetricName " : " ThreatIntelIndicator " ,

" Namespace " : " AWS/GuardDuty " ,

" Statistic " : " Sum " ,

" Dimensions " : [

{

" Name " : " FindingType " ,

" Value " : " Recon:EC2/Portscan "

}

],

" Period " : 300,

" EvaluationPeriods " : 1,

" Threshold " : 1,

" ComparisonOperator " : " GreaterThanOrEqualToThreshold " ,

" AlarmActions " : [ " arn:aws:sns:region:account-id:SecurityAlerts " ]

}

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-reference-importvalue.html

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/intrinsic-function-reference-importvalue.html CF of network exports the VPC, subnet or needed information CF of application imports the above information to its stack and UpdateChangeSet/ ExecuteChangeSet

https://docs.aws.amazon.com/systems-manager/latest/userguide/sysman-managed-instance-activation.html

 Add a Deploy Stage to the Pipeline, Configure Amazon ECS as the Action Provider:

By adding a deploy stage to the pipeline and configuring Amazon ECS as the action provider, the pipeline can automatically deploy the new container image to the ECS cluster.

This ensures that the service is updated with the new image tag, making the new version of the service endpoint reachable.

The latency in processing and ingesting logs can be caused by several factors, such as the throughput of the Kinesis data stream, the concurrency of the Lambda function, and the configuration of the event source mapping. To reduce the latency, the following steps can be taken:

Create a data stream consumer with enhanced fan-out. Set the Lambda function that processes the logs as the consumer. This will allow the Lambda function to receive records from the data stream with dedicated throughput of up to 2 MB per second per shard, independent of other consumers1. This will reduce the contention and delay in accessing the data stream.

Increase the ParallelizationFactor setting in the Lambda event source mapping. This will allow the Lambda service to invoke more instances of the function concurrently to process the records from the data stream2. This will increase the processing capacity and reduce the backlog of records in the data stream.

Configure reserved concurrency for the Lambda function that processes the logs. This will ensure that the function has enough concurrency available to handle the increased load from the data stream3. This will prevent the function from being throttled by the account-level concurrency limit.

The other options are not effective or may have negative impacts on the latency. Option D is not suitable because increasing the batch size in the Kinesis data stream will increase the amount of data that the Lambda function has to process in each invocation, which may increase the execution time and latency4. Option E is not advisable because turning off the ReportBatchItemFailures setting in the Lambda event source mapping will prevent the Lambda service from retrying the failed records, which may result in data loss. Option F is not necessary because increasing the number of shards in the Kinesis data stream will increase the throughput of the data stream, but it will not affect the processing speed of the Lambda function, which is the bottleneck in this scenario.

1: Using AWS Lambda with Amazon Kinesis Data Streams - AWS Lambda

2: AWS Lambda event source mappings - AWS Lambda

3: Managing concurrency for a Lambda function - AWS Lambda

4: AWS Lambda function scaling - AWS Lambda

AWS Lambda event source mappings - AWS Lambda

Scaling Amazon Kinesis Data Streams with AWS CloudFormation - Amazon Kinesis Data Streams

The best solution to log and review all stopped tasks for errors is to use Amazon EventBridge and Amazon CloudWatch Logs. Amazon EventBridge allows the DevOps engineer to create a rule that matches task state change events from Amazon ECS. The rule can then send the event data to Amazon CloudWatch Logs as the target. Amazon CloudWatch Logs can store and monitor the log data, and also provide CloudWatch Logs Insights, a feature that enables the DevOps engineer to interactively search and analyze the log data. Using CloudWatch Logs Insights, the DevOps engineer can filter and aggregate the log data based on various fields, such as cluster, task, container, and reason. This way, the DevOps engineer can easily identify and investigate the stopped tasks and their errors.

The other options are not as effective or efficient as the solution in option A. Option B is not suitable because the embedded metric format is designed for custom metrics, not for logging task state changes. Option C is not feasible because the EC2 instances do not store the task state change events in their logs. Option D is not relevant because the EC2_INSTANCE_TERMINATING lifecycle hook is triggered when an EC2 instance is terminated by the Auto Scaling group, not when a task is stopped by Amazon ECS.

Creating a CloudWatch Events Rule That Triggers on an Event - Amazon Elastic Container Service

Sending and Receiving Events Between AWS Accounts - Amazon EventBridge

Working with Log Data - Amazon CloudWatch Logs

Analyzing Log Data with CloudWatch Logs Insights - Amazon CloudWatch Logs

Embedded Metric Format - Amazon CloudWatch

Amazon EC2 Auto Scaling Lifecycle Hooks - Amazon EC2 Auto Scaling

To meet the requirements, the DevOps engineer should create an Amazon EventBridge event rule that has the default event bus as the source. The rule ' s event pattern should match EC2 security group creation and modification events, and it should be configured to invoke the Lambda function. This solution will allow for near real-time detection of security group rule changes and will trigger the Lambda function to remove any unrestricted rules and send email notifications to the security team.

https://repost.aws/knowledge-center/monitor-security-group-changes-ec2

To allow only creation/configuration of service-linked roles , you need a way to tightly scope what IAM actions the developer can perform. The most AWS-appropriate mechanism for “delegate limited IAM admin” is a role with a permissions boundary :

A permissions boundary sets the maximum permissions the role (and any roles it creates, depending on design) can ever have. This is a standard pattern to safely delegate IAM tasks without granting broad IAM administration.

You can define the role’s policy + boundary so the developer can call only the APIs required for service-linked roles (for example actions like creating service-linked roles and passing only allowed AWS service principals), while preventing general role/policy creation outside that scope.

Why the others don’t meet “service-linked roles only”:

A is vague (“common services”) and cross-account access doesn’t inherently restrict to only service-linked roles. It’s also more operational overhead than needed for a single dev account use case.

B PowerUserAccess is far too broad and does not restrict to service-linked roles.

C An SCP with Deny iam:* would block IAM entirely (including what the developer needs). Even if refined, SCPs set account-wide guardrails and are not the right tool to grant a single developer the precise ability to create only service-linked roles.

https://docs.aws.amazon.com/config/latest/developerguide/ec2-ebs-encryption-by-default.html

When you create a pipeline from CodePipeline during the step-by-step it creates a CloudWatch Event rule for a given branch and repo

like this:

{

" source " : [

" aws.codecommit "

],

" detail-type " : [

" CodeCommit Repository State Change "

],

" resources " : [

" arn:aws:codecommit:us-east-1:xxxxx:repo-name "

],

" detail " : {

" event " : [

" referenceCreated " ,

" referenceUpdated "

],

" referenceType " : [

" branch "

],

" referenceName " : [

" master "

]

}

}

https://docs.aws.amazon.com/codepipeline/latest/userguide/pipelines-trigger-source-repo-changes-console.html

In the source AWS account, the IAM role used by the CI/CD pipeline should have permissions to access the source code repository, build artifacts, and any other resources required for the build process. In the destination AWS accounts, the IAM role used for deployment should have permissions to access the AWS resources required for deploying the application, such as EC2 instances, RDS databases, S3 buckets, etc. The exact permissions required will depend on the specific resources being used by the application. the IAM role used for deployment in the destination accounts should also have permissions to assume the IAM role for deployment in the centralized DevOps account. This is typically done using an IAM role trust policy that allows the destination account to assume the DevOps account role.

Option B is the correct choice and has the least operational overhead because ECR lifecycle policies are the native, managed feature built specifically to automate image cleanup:

ECR lifecycle policies can expire images based on image age (e.g., images older than X days) and can also enforce retaining a minimum number of images (count-based retention), which directly matches the requirement “retain at least a specified number of images.”

Lifecycle policies work for both tagged and untagged images using rule selection and tagging status filters, so the repository stays clean without custom automation.

Why the other options are not appropriate / higher overhead:

A is invalid: ECR is not S3; S3 lifecycle policies do not manage ECR images .

C and D introduce custom automation (Lambda or SSM scripting), scheduling, permissions, error handling, retries, logging, and ongoing maintenance—higher overhead than a managed lifecycle policy.

The requirement is classic attribute-based access control (ABAC) : users should only access EC2 resources whose resource tag matches the user’s department attribute coming from IAM Identity Center (mapped into a principal/session tag like aws:PrincipalTag/department).

For EC2 resource authorization using tags, the correct pattern is to compare the EC2 resource tag condition key ec2:ResourceTag/department to the principal tag ${aws:PrincipalTag/department} . That is exactly what Option C does.

Why the other options are wrong:

A only checks that the tag key exists (aws:TagKeys) and does not enforce a match between user department and instance department.

B uses aws:ResourceTag/... rather than the EC2-specific tag key typically used for EC2 authorization decisions in EC2 actions (ec2:ResourceTag/...).

D restricts access to resources tagged as d1/d2/d3 but does not ensure the user can only access their own department.

A bucket policy is a resource-based policy that defines who can access a specific S3 bucket and what actions they can perform on it. By removing unauthenticated access from the bucket policy, you can prevent anyone without valid credentials from accessing the bucket. A service role is an IAM role that allows an AWS service, such as CodeBuild, to perform actions on your behalf. By modifying the service role for the CodeBuild project to include Amazon S3 access, you can grant the project permission to read and write objects in the S3 bucket. The AWS CLI is a command-line tool that allows you to interact with AWS services, such as S3, using commands in your terminal. By using the AWS CLI to download the database population script, you can leverage the service role credentials and encryption to secure the data transfer.

For more information, you can refer to these web pages:

[Using bucket policies and user policies - Amazon Simple Storage Service]

[Create a service role for CodeBuild - AWS CodeBuild]

[AWS Command Line Interface]

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html#LambdaFunctionExample

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/SubscriptionFilters.html

 Create an Amazon Elastic File System (Amazon EFS) File System with Targets in Multiple Availability Zones:

Amazon EFS provides a scalable and highly available network file system that supports the NFS protocol. EFS is ideal for Linux instances as it allows multiple instances to read and write data concurrently.

Setting up EFS with targets in multiple Availability Zones ensures high availability and durability.

The question emphasizes “MOST securely” and involves a multi-account backup strategy with a centralized backup account. AWS Backup best practice for high-security environments is to use customer managed KMS keys for encrypting backup vaults rather than AWS managed keys. Customer managed keys provide tighter control over key lifecycle, key policies, and cross-account access.

Option A creates encrypted backup vaults in both the source and centralized accounts, each protected by a customer managed KMS key in that account. AWS Backup is then configured to create full EC2 backups (AMIs) and copy them to the centralized backup vault. This design ensures:

Encryption at rest in both accounts.

Independent key control and policies per account.

Secure cross-account backup copy behavior governed by explicit KMS key grants and vault access policies.

Option B incorrectly uses the source account’s KMS key to encrypt vaults in both accounts, which is not the recommended pattern; each account should manage its own CMK. Options C and D rely partially or fully on AWS managed keys, which are less appropriate when the requirement explicitly stresses maximum security and control.

Therefore, Option A best satisfies both the RPO/RTO goals (daily AMI backups with rapid restore) and the strongest encryption posture.

Option A is incorrect because creating an Amazon EventBridge rule that invokes an AWS Lambda function to query the applications on a 5-minute interval is not a valid solution. EventBridge rules can only trigger Lambda functions based on events, not on time intervals. Moreover, querying the applications on a 5-minute interval might incur unnecessary costs and network overhead, and might not detect performance issues in real time.

Option B is correct because creating an Amazon CloudWatch Synthetics canary that runs a custom script to query the applications on a 5-minute interval is a valid solution. CloudWatch Synthetics canaries are configurable scripts that monitor endpoints and APIs by simulating customer behavior. Canaries can run as often as once per minute, and can measure the latency and availability of the applications. Canaries can also send notifications to an Amazon SNS topic when they detect errors or performance issues1.

Option C is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution. The RequestCountPerTarget metric measures the number of requests completed or connections made per target in a target group2. This metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the number of connections becomes greater than the configured number of threads that the application supports is not a valid way to measure the application performance, as it depends on the application design and implementation.

Option D is incorrect because creating an Amazon CloudWatch alarm that uses the AWS/ApplicationELB namespace RequestCountPerTarget metric is not a valid solution, for the same reason as option C. The RequestCountPerTarget metric does not reflect the application response time, which is the requirement. Moreover, configuring the CloudWatch alarm to send a notification when the average response time becomes greater than the longest response time that the application supports is not a valid way to measure the application performance, as it does not account for variability or outliers in the response time distribution.

To implement a more reliable deployment solution, a DevOps engineer should take the following actions:

Create a pipeline in AWS CodePipeline that uses the CodeCommit repository as a source provider. Configure pipeline stages that run the CodeBuild project in parallel to build and test the application. In the pipeline, pass the CodeBuild project output artifact to an AWS CodeDeploy action. This action will improve the deployment reliability by automating the entire process from code commit to deployment, reducing human errors and inconsistencies. By running the build and test stages in parallel, the pipeline can also speed up the delivery time and provide faster feedback. By using CodeDeploy as the deployment action, the pipeline can leverage the features of CodeDeploy, such as traffic shifting, health checks, rollback, and deployment configuration123

Create an AWS CodeDeploy application and a deployment group to deploy the packaged code to the EC2 instances. Configure the ALB for the deployment group. This action will improve the deployment reliability by using CodeDeploy to orchestrate the deployment across multiple EC2 instances behind an ALB. CodeDeploy can perform blue/green deployments or in-place deployments with traffic shifting, which can minimize downtime and reduce risks. CodeDeploy can also monitor the health of the instances during and after the deployment, and automatically roll back if any issues are detected. By configuring the ALB for the deployment group, CodeDeploy can register and deregister instances from the load balancer as needed, ensuring that only healthy instances receive traffic45

The other options are not correct because they do not improve the deployment reliability or follow best practices. Creating separate pipeline stages that run a CodeBuild project to build and then test the application is not a good option because it will increase the pipeline execution time and delay the feedback loop. Creating individual Lambda functions that use CodeDeploy instead of Systems Manager to run build, test, and deploy actions is not a valid option because it will add unnecessary complexity and cost to the solution. Lambda functions are not designed for long-running tasks such as building or deploying applications. Creating an Amazon S3 bucket and modifying the CodeBuild project to store the packages in the S3 bucket instead of in CodeArtifact is not a necessary option because it will not affect the deployment reliability. CodeArtifact is a secure, scalable, and cost-effective package management service that can store and share software packages for application development67

1: What is AWS CodePipeline? - AWS CodePipeline

2: Create a pipeline in AWS CodePipeline - AWS CodePipeline

3: Deploy an application with AWS CodeDeploy - AWS CodePipeline

4: What is AWS CodeDeploy? - AWS CodeDeploy

5: Configure an Application Load Balancer for your blue/green deployments - AWS CodeDeploy

6: What is AWS Lambda? - AWS Lambda

7: What is AWS CodeArtifact? - AWS CodeArtifact

This solution will meet the requirements because it will use CloudFormation nested stacks and stack sets to deploy the templates more efficiently and consistently across multiple regions. Nested stacks allow the company to separate out common components and reuse templates, while stack sets allow the company to create stacks in multiple accounts and regions with a single template. The company can also use Amazon SNS to send notifications to the data engineering team whenever a change is made to the templates or the stacks. Amazon SNS is a service that allows you to publish messages to subscribers, such as email addresses, phone numbers, or other AWS services. By using Amazon SNS, the company can ensure that the data engineering team is aware of all changes to the templates and can take appropriate actions if needed. What is Amazon SNS? - Amazon Simple Notification Service

https://docs.aws.amazon.com/storagegateway/latest/APIReference/API_RefreshCache.html " It only updates the cached inventory to reflect changes in the inventory of the objects in the S3 bucket. This operation is only supported in the S3 File Gateway types. "

The correct answer is C. Updating the SCP in the child OU to allow all actions for Amazon EC2 will enable the DevOps engineer to launch the EC2 instance in the vendor-data account. SCPs are applied to OUs and accounts in a hierarchical manner, meaning that the SCPs attached to the parent OU are inherited by the child OU and accounts. Therefore, the SCP in the child OU overrides the SCP in the root OU and denies all actions except for DynamoDB and Lambda. By adding EC2 to the allowed actions in the child OU’s SCP, the DevOps engineer can access EC2 resources in the vendor-data account.

Option A is incorrect because attaching the AmazonEC2FullAccess IAM policy to the IAM user will not grant the user access to EC2 resources. IAM policies are evaluated after SCPs, so even if the IAM policy allows EC2 actions, the SCP will still deny them.

Option B is incorrect because creating a new SCP that allows all actions for EC2 and attaching it to the vendor-data account will not work. SCPs are not cumulative, meaning that only one SCP is applied to an account at a time. The SCP attached to the account will be the SCP attached to the OU that contains the account. Therefore, option B will not change the SCP that is applied to the vendor-data account.

Option D is incorrect because creating a new SCP that allows all actions for EC2 and attaching it to the root OU will not work. As explained earlier, the SCP in the child OU overrides the SCP in the root OU and denies all actions except for DynamoDB and Lambda. Therefore, option D will not affect the SCP that is applied to the vendor-data account.

Deployment of config.txt file based on the appspec.yml:

The appspec.yml file specifies that config/config.txt should be copied to /usr/local/src/config.txt.

The source: / directive in the appspec.yml indicates that the entire directory structure starting from the root of the application source should be copied to the specified destination, which is /var/www/html.

Result of the Deployment:

The config.txt file will be specifically deployed to /usr/local/src/config.txt as per the explicit file mapping.

The entire directory structure including application/web will be copied to /var/www/html, but this does not include config/config.txt since it has a specific destination defined.

Thus, the config.txt file will be deployed only to /usr/local/src/config.txt.

Therefore, the correct answer is:

C. The config.txt file will be deployed to only /usr/local/src/config.txt.

AWS Secrets Manager supports managed rotation using Lambda templates for ElastiCache (Redis). Secrets Manager can integrate with Parameter Store using references ({{resolve:secretsmanager:...}}). This ensures automatic rotation with minimal management.

Only the Organizations management account (or roles in that account) can call organizations:CreateAccount directly. When moving the Lambda function to a different (dedicated) account, the correct pattern is to perform cross-account role assumption into a role that resides in the management account and has tightly scoped Organizations permissions.

Option A describes exactly this:

Create an IAM role in the management account with only the required Organizations permissions (for example, organizations:CreateAccount and related read permissions).

Configure the trust policy on that role to allow it to be assumed by the Lambda execution role in the new account.

Update the Lambda code to call sts:AssumeRole into the management-account role before invoking the Organizations API.

This approach ensures that:

The Lambda function can create new accounts, but only via the management account.

The Lambda execution role in the dedicated account has no direct Organizations permissions; it only has permission to assume the specific role.

Option B is incorrect because “delegated administration for Organizations” in the generic sense is not how CreateAccount is exposed; account creation remains a management-account responsibility. Options C and D either misconfigure trust or introduce unnecessary Control Tower complexity. Cross-account assume-role from the dedicated account into the management account is the correct and least-privilege solution.

Accessing a secret from AWS Secrets Manager that is encrypted with a customer managed KMS key requires two distinct permission layers to succeed:

Secrets Manager permissions (via IAM) such as secretsmanager:GetSecretValue for the IAM role assumed by the Kubernetes service account.

KMS key usage permissions in the KMS key policy , allowing the same IAM principal to perform kms:Decrypt (and possibly kms:GenerateDataKey ) on the customer managed key.

In this scenario, the service account assumes an IAM role explicitly created to access the secrets. The symptom is an HTTP 403 “Access Denied” when retrieving the secret. If the IAM role has Secrets Manager permissions but the call still fails, the typical root cause is that the KMS key policy does not trust or allow that IAM role , so the decrypt operation fails.

Option B correctly identifies that the KMS key policy must include the Kubernetes service account IAM role as a principal with the appropriate KMS actions.

Option A and C refer to the EKS cluster IAM role, which is not the principal making the call. Option D is unrelated: the role’s ability to “access the EKS cluster” does not affect its ability to call Secrets Manager or KMS. The missing KMS key policy permission is the underlying cause.

 Configure AWS Systems Manager on Each Instance:

AWS Systems Manager provides a unified interface for managing AWS resources. Install the Systems Manager agent on each EC2 instance to enable inventory management and other features.

 Use AWS Systems Manager Inventory:

Systems Manager Inventory collects metadata about your instances and the software installed on them. This data includes information about applications, network configurations, and more.

Enable Systems Manager Inventory on all EC2 instances to gather detailed information about installed applications.

 Use Systems Manager Resource Data Sync to Synchronize and Store Findings in an Amazon S3 Bucket:

Resource Data Sync aggregates inventory data from multiple accounts and regions into a single S3 bucket, making it easier to query and analyze the data.

Configure Resource Data Sync to automatically transfer inventory data to an S3 bucket for centralized storage.

 Create an AWS Lambda Function that Runs When New Objects are Added to the S3 Bucket:

Use an S3 event to trigger a Lambda function whenever new inventory data is added to the S3 bucket.

The Lambda function can parse the inventory data and check for the presence of prohibited applications.

 Configure the Lambda Function to Identify Prohibited Applications:

The Lambda function should be programmed to scan the inventory data for any known prohibited applications and generate alerts or take appropriate actions if such applications are found.

Example Lambda function in Python

import json

import boto3

def lambda_handler(event, context):

s3 = boto3.client( ' s3 ' )

bucket = event[ ' Records ' ][0] [ ' s3 ' ][ ' bucket ' ][ ' name ' ]

key = event[ ' Records ' ][0] [ ' s3 ' ][ ' object ' ][ ' key ' ]

response = s3.get_object(Bucket=bucket, Key=key)

inventory_data = json.loads(response[ ' Body ' ].read().decode( ' utf-8 ' ))

prohibited_apps = [ ' app1 ' , ' app2 ' ]

for instance in inventory_data[ ' Instances ' ]:

for app in instance[ ' Applications ' ]:

if app[ ' Name ' ] in prohibited_apps:

# Send notification or take action

print(f " Prohibited application found: {app[ ' Name ' ]} on instance {instance[ ' InstanceId ' ]} " )

return { ' statusCode ' : 200, ' body ' : json.dumps( ' Check completed ' )}

By leveraging AWS Systems Manager Inventory, Resource Data Sync, and Lambda, this solution provides an efficient and automated way to audit EC2 instances for prohibited applications.

To implement failover for the application to the secondary Region so that HTTP GET requests meet the desired RTO, the DevOps engineer should use the following solution:

Create a new origin on the distribution for the secondary ALB. A CloudFront origin is the source of the content that CloudFront delivers to viewers. By creating a new origin for the secondary ALB, the DevOps engineer can configure CloudFront to route traffic to the secondary Region when the primary Region is unavailable1

Create a new origin group. Set the original ALB as the primary origin. Configure the origin group to fail over for HTTP 5xx status codes. An origin group is a logical grouping of two origins: a primary origin and a secondary origin. By creating an origin group, the DevOps engineer can specify which origin CloudFront should use as a fallback when the primary origin fails. The DevOps engineer can also define which HTTP status codes should trigger a failover from the primary origin to the secondary origin. By setting the original ALB as the primary origin and configuring the origin group to fail over for HTTP 5xx status codes, the DevOps engineer can ensure that CloudFront will switch to the secondary ALB when the primary ALB returns server errors2

Update the default behavior to use the origin group. A behavior is a set of rules that CloudFront applies when it receives requests for specific URLs or file types. The default behavior applies to all requests that do not match any other behaviors. By updating the default behavior to use the origin group, the DevOps engineer can enable failover routing for all requests that are sent to the distribution3

This solution will meet the requirements because it will automate the failover of the application to the secondary Region with zero-second RTO. When CloudFront receives an HTTP GET request, it will first try to route it to the primary ALB in the primary Region. If the primary ALB is healthy and returns a successful response, CloudFront will deliver it to the viewer. If the primary ALB is unhealthy or returns an HTTP 5xx status code, CloudFront will automatically route the request to the secondary ALB in the secondary Region and deliver its response to the viewer.

The other options are not correct because they either do not provide zero-second RTO or do not work as expected. Creating a second CloudFront distribution that has the secondary ALB as the default origin and creating Amazon Route 53 alias records that have a failover policy is not a good option because it will introduce additional latency and complexity to the solution. Route 53 health checks and DNS propagation can take several minutes or longer, which means that viewers might experience delays or errors when accessing the application during a failover event. Creating Amazon Route 53 alias records that have a failover policy and Evaluate Target Health set to Yes for both ALBs and setting the TTL of both records to O is not a valid option because it will not work with CloudFront distributions. Route 53 does not support health checks for alias records that point to CloudFront distributions, so it cannot detect if an ALB behind a distribution is healthy or not. Creating a CloudFront function that detects HTTP 5xx status codes and returns a 307 Temporary Redirect error response to the secondary ALB is not a valid option because it will not provide zero-second RTO. A 307 Temporary Redirect error response tells viewers to retry their requests with a different URL, which means that viewers will have to make an additional request and wait for another response from CloudFront before reaching the secondary ALB.

1: Adding, Editing, and Deleting Origins - Amazon CloudFront

2: Configuring Origin Failover - Amazon CloudFront

3: Creating or Updating a Cache Behavior - Amazon CloudFront

This is a classic AWS Organizations governance design:

Restrict Regions and allowed services across all accounts

The correct Organizations mechanism for account-wide guardrails is a Service Control Policy (SCP) .

SCPs set the maximum permissions that accounts/OUs can use, making them the right tool to enforce “only these Regions” and “only these services” consistently across the org.

Authentication from Active Directory + consistent job-function roles in every account

With AD as the identity source, the standard approach is federation to AWS using an IAM identity provider (SAML) and role assumption based on job function.

AWS CloudFormation StackSets is the operationally efficient way to deploy identical IAM roles/policies into multiple accounts/OUs so job-function permissions are consistent everywhere.

The roles’ trust policies can be configured to allow federated identities (from the AD-backed IdP) to assume the correct job role in each account.

Why the other options don’t fit:

A “OU with group policies” isn’t an AWS Organizations control mechanism for restricting Regions/services. The AWS-native control for this is SCPs , not “group policies.”

B Permissions boundaries are useful to limit what an IAM principal/role can do within an account , but they are not the org-wide enforcement mechanism for “all accounts must stay in these Regions / use only these services.” That’s what SCPs do.

C AWS RAM is for sharing resources (like subnets, Transit Gateway, etc.), not for “sharing roles” across accounts as a governance baseline. Also, the question emphasizes identical permissions in each account, which is best achieved by provisioning roles in each account (StackSets), not attempting to “share” roles.

Option A is correct because using batch writes to write multiple log events in a single write operation is a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Batch writes can reduce the number of network round trips and API calls, and can also take advantage of parallel processing by Timestream. Batch writes can also improve the compression ratio of data in the memory store and the magnetic store, which can reduce the storage costs and improve the query performance1.

Option B is incorrect because writing each log event as a single write operation is not a recommended practice for optimizing the performance and cost of data ingestion in Timestream. Writing each log event as a single write operation would increase the number of network round trips and API calls, and would also reduce the compression ratio of data in the memory store and the magnetic store. This would increase the storage costs and degrade the query performance1.

Option C is incorrect because treating each log as a single-measure record is not a recommended practice for optimizing the query performance in Timestream. Treating each log as a single-measure record would result in creating multiple records for each timestamp, which would increase the storage size and the query latency. Moreover, treating each log as a single-measure record would require using joins to query multiple measures for the same timestamp, which would add complexity and overhead to the query processing2.

Option D is correct because treating each log as a multi-measure record is a recommended practice for optimizing the query performance in Timestream. Treating each log as a multi-measure record would result in creating a single record for each timestamp, which would reduce the storage size and the query latency. Moreover, treating each log as a multi-measure record would allow querying multiple measures for the same timestamp without using joins, which would simplify and speed up the query processing2.

Option E is incorrect because configuring the memory store retention period to be longer than the magnetic store retention period is not a valid option in Timestream. The memory store retention period must always be shorter than or equal to the magnetic store retention period. This ensures that data is moved from the memory store to the magnetic store before it expires out of the memory store3.

Option F is correct because configuring the memory store retention period to be shorter than the magnetic store retention period is a valid option in Timestream. The memory store retention period determines how long data is kept in the memory store, which is optimized for fast point-in-time queries. The magnetic store retention period determines how long data is kept in the magnetic store, which is optimized for fast analytical queries. By configuring these retention periods appropriately, you can balance your storage costs and query performance according to your application needs3.

https://aws.amazon.com/blogs/mt/how-to-centrally-manage-aws-iot-greengrass-devices-using-aws-systems-manager/?force_isolation=true

This solution will meet the requirements in the most operationally efficient manner because it will use CloudWatch Logs destination to aggregate the log groups from all the accounts to a single S3 bucket in the logging account. However, unlike option A, this solution will create a CloudWatch Logs destination for each region, instead of a single destination for all regions. This will improve the performance and reliability of the log delivery, as it will avoid cross-region data transfer and latency issues. Moreover, this solution will use an Amazon Kinesis data stream and an Amazon Kinesis Data Firehose delivery stream for each region, instead of a single stream for all regions. This will also improve the scalability and throughput of the log delivery, as it will avoid bottlenecks and throttling issues that may occur with a single stream.

To meet the new guideline for application deployment, the company can use a combination of AWS CodePipeline and AWS CloudTrail. A manual approval action in CodePipeline allows the security team to review and approve changes before they are deployed. This action can be configured to pause the pipeline until approval is granted, ensuring that no changes move to production without the necessary sign-off. Additionally, by creating an AWS CloudTrail trail, all actions taken within CodePipeline, including approvals, are recorded and delivered to an Amazon S3 bucket. This provides an audit trail that can be retained for compliance and review purposes.

AWS CodePipeline’s manual approval action provides a way to ensure that a member of the security team can review and approve changes before they are deployed1.

AWS CloudTrail integration with CodePipeline allows for the recording and retention of all pipeline actions, including approvals, which can be stored in Amazon S3 for record-keeping2.

The correct solution is to add a report group in the AWS CodeBuild project buildspec file with the appropriate path and format for the reports. Then, create an Amazon S3 bucket to store the reports. You should configure an Amazon EventBridge rule that invokes an AWS Lambda function to copy the reports to the S3 bucket when a build is completed. Finally, create an S3 Lifecycle rule to expire the objects after 90 days. This approach allows for the automated transfer of reports to long-term storage and ensures they are retained for the required duration without manual intervention1.

AWS CodeBuild User Guide on test reporting1.

AWS CodeBuild User Guide on working with report groups2.

AWS Documentation on using AWS CodePipeline with AWS CodeBuild3.

To meet the requirements of deploying a workload on several hundred EC2 instances with least-privilege permissions and temporary security credentials, the company should use an IAM role and an instance profile. An IAM role is a way to grant permissions to an entity that you trust, such as an EC2 instance. An instance profile is a container for an IAM role that you can use to pass role information to an EC2 instance when the instance starts. By using an IAM role and an instance profile, the EC2 instances can automatically receive temporary security credentials from the AWS Security Token Service (STS) and use them to access the S3 buckets. This way, the company does not need to manage or rotate any long-term credentials, such as IAM users or access keys.

To use an IAM role and an instance profile, the company should create an IAM role that has the appropriate permissions for S3 buckets. The permissions should allow the EC2 instances to read from the source S3 bucket and write to the destination S3 bucket. The company should also create a trust policy for the IAM role that specifies that EC2 is allowed to assume the role. Then, the company should add the IAM role to an instance profile. An instance profile can have only one IAM role, so the company does not need to create multiple roles or profiles for this scenario.

Next, the company should update the launch template to include the IAM instance profile. A launch template is a way to save launch parameters for EC2 instances, such as the instance type, security group, user data, and IAM instance profile. By using a launch template, the company can ensure that all EC2 instances in the Auto Scaling group have consistent configuration and permissions. The company should specify the name or ARN of the IAM instance profile in the launch template. This way, when the Auto Scaling group launches new EC2 instances based on the launch template, they will automatically receive the IAM role and its permissions through the instance profile.

The other options are not correct because they do not meet the requirements or follow best practices. Creating an IAM user and generating a secret key and token is not a good option because it involves managing long-term credentials that need to be rotated regularly. Moreover, embedding credentials in user data is not secure because user data is visible to anyone who can describe the EC2 instance. Creating a trust anchor and profile is not a valid option because trust anchors are used for certificate-based authentication, not for IAM roles or instance profiles. Modifying user data to use a new secret key and token is also not a good option because it requires updating user data every time the credentials change, which is not scalable or efficient.

1: AWS Certified DevOps Engineer - Professional Certification | AWS Certification | AWS

2: DevOps Resources - Amazon Web Services (AWS)

3: Exam Readiness: AWS Certified DevOps Engineer - Professional

IAM Roles for Amazon EC2 - AWS Identity and Access Management

Working with Instance Profiles - AWS Identity and Access Management

Launching an Instance Using a Launch Template - Amazon Elastic Compute Cloud

Temporary Security Credentials - AWS Identity and Access Management

This hook runs before the new application version is installed on the replacement instances. This is the best place to run the script because it ensures that the license file is downloaded and installed before the replacement instances start to handle request traffic. If you use any other hook, you may encounter errors or inconsistencies in your application.

AWS Config can monitor compliance by checking EC2 software inventory. Custom Config rules can automatically trigger Systems Manager Automation documents as remediation actions when noncompliant (software missing). This meets policy enforcement goals with automation.

https://docs.aws.amazon.com/codedeploy/latest/userguide/deployment-steps.html

The core problem described is deployment inconsistency and lack of reliability caused by using AWS Systems Manager Run Command for application deployment. Run Command executes ad hoc commands and does not provide deployment orchestration, version tracking, lifecycle hooks, or health-based traffic control, which commonly leads to drift between EC2 instances.

The most reliable AWS-native solution is to adopt AWS CodePipeline combined with AWS CodeDeploy . Option B introduces a structured CI/CD pipeline with a clear build stage followed by a test stage , ensuring that only tested artifacts progress to deployment. Sequential build and test stages are preferred for reliability and deterministic behavior, especially when test results must gate deployments.

Option C is essential because AWS CodeDeploy is the service specifically designed to deploy application revisions consistently across EC2 fleets. By creating a CodeDeploy application and deployment group and integrating it with the ALB, deployments gain support for lifecycle events, health checks, instance synchronization, and automatic rollback. This ensures that all EC2 instances receive the same application version and that traffic is managed safely during deployments.

Option A introduces unnecessary parallelism that does not address the core issue. Option D adds excessive complexity with Lambda orchestration. Option E incorrectly replaces CodeArtifact with S3 without addressing deployment reliability.

Therefore, combining CodePipeline (B) with CodeDeploy and ALB integration (C) provides consistent, repeatable, and reliable deployments aligned with AWS best practices.

https://repost.aws/knowledge-center/cloudformation-update-rollback-failed If your stack is stuck in the UPDATE_ROLLBACK_FAILED state after a failed update, then the only actions that you can perform on the stack are the ContinueUpdateRollback or DeleteStack operations.

The key requirement is to prevent non-approved EC2 instance types from being created at stack creation time , specifically when developers deploy AWS CloudFormation stacks. This is a pre-deployment enforcement requirement, not a detection or remediation requirement after resources already exist.

CloudFormation Guard Hooks are purpose-built for this use case. They allow organizations to define policy-as-code rules that validate CloudFormation templates before resources are created or updated. By writing a CloudFormation Guard rule that explicitly checks the InstanceType property against an approved allow list, the stack operation will fail immediately if a developer attempts to use a disallowed instance type. This enforces compliance early in the deployment lifecycle and avoids post-deployment cleanup.

Option B uses AWS Config, which only detects noncompliance after resources are created. Even with remediation, the instance would still be launched briefly, which violates the requirement. Option C uses an SCP, which applies broadly to all EC2 launches in the organization and is not limited to CloudFormation stacks; this is overly restrictive and could unintentionally block other valid use cases. Option A incorrectly combines Lambda with Guard Hooks—Guard Hooks natively evaluate Guard rules and do not invoke Lambda functions.

Therefore, using a CloudFormation Guard rule with a Guard Hook is the correct and AWS-recommended solution for enforcing approved EC2 instance types during CloudFormation deployments.

The recommended architecture to audit, analyze, and visualize application workload metrics at scale involves:

Using Amazon EventBridge to schedule AWS Lambda invocations that call the application API and fetch metrics (Option A). The data is stored in Amazon S3, which is ideal for scalable, cost-effective storage of large datasets.

Cataloging the stored data with AWS Glue crawlers, enabling schema discovery and making data queryable via Amazon Athena (Option C).

Visualizing the data by creating Amazon QuickSight datasets from Athena views and building dashboards for analysis (Option E). Option B and D introduce DynamoDB, which is less suitable for large-scale analytics and Athena querying. Option F suggests querying Athena via Lambda widgets in CloudWatch, which adds complexity without significant benefit over QuickSight.

Account Factory Customization (AFC) automates Control Tower account provisioning with baseline blueprints for networking, logging, and guardrails. It integrates natively and minimizes overhead, per AWS Control Tower documentation.

The requirements describe a classic attribute-based access control (ABAC) model using AWS IAM. The company already has consistent tagging across users and resources, which is ideal for ABAC and least-privilege enforcement at scale.

First, Option A is required because IAM roles should represent job functions (developer vs. database administrator) and be scoped per project. Creating separate roles per project and job function allows permissions to be isolated cleanly and prevents cross-project access.

Second, Option B enforces project-level isolation at the policy level . By modifying the existing job-function policies to include a StringEquals condition that compares aws:ResourceTag/Project with aws:PrincipalTag/Project, access is automatically limited to only those resources that belong to the same project as the user. This is an AWS-recommended ABAC pattern and avoids policy sprawl.

Third, Option C is required so users can assume roles only when their tags match the role’s tags . An IAM policy attached to users that allows sts:AssumeRole only if both project and job function tags match ensures users cannot assume roles outside their assigned scope.

Options D, E, and F either misapply policies to the wrong entities, misuse tagging on policies (which IAM does not evaluate for authorization), or introduce unnecessary group-based management that conflicts with the ABAC design.

Therefore, A, B, and C together provide least privilege, project isolation, and scalable access control with minimal ongoing administration.

Amazon DevOps Guru is a fully managed service that uses machine learning to detect anomalous application behavior and provides insights with recommended remediation actions to improve operational performance.

Enabling DevOps Guru (Option A) covers many AWS resources automatically and reduces manual analysis.

Configuring SNS notifications (Option B) allows the team to be proactively alerted about important events.

Options C, D, and E require manual data setup, complex querying, and dashboard/reporting maintenance, which increase operational overhead. Therefore, the combination of enabling DevOps Guru and setting up notifications achieves the goals with minimal effort.

Amazon GuardDuty provides built-in, fully managed threat detection for AWS accounts and workloads, including automatic detection of cryptocurrency mining activity, compromised EC2 instances, command-and-control traffic patterns, and anomalous behaviors. GuardDuty includes multiple threat identifiers such as EC2:CryptoCurrencyActivity.BitcoinToolDetected and EC2:UnauthorizedAccess.CryptoMining. Because this capability is native and requires no custom detection logic, it provides the lowest development and operational overhead compared to alternatives.

When GuardDuty generates a finding, it publishes the event automatically to Amazon EventBridge. The DevOps engineer can create an EventBridge rule that matches the specific mining-related finding types. The target of the rule is an AWS Lambda function that parses instance details from the finding and calls TerminateInstances on the affected EC2 instance.

This enables real-time automated remediation without needing recurring scans, log parsing, or complex data queries.

Options A and B require building custom detection pipelines and maintaining periodic Lambda polling, increasing long-term complexity. Option D (Security Hub) aggregates findings but still relies on GuardDuty for detection, adding unnecessary overhead.

Therefore, GuardDuty + EventBridge + Lambda termination is the easiest, quickest, and most AWS-recommended solution following incident-response best practices.

https://docs.aws.amazon.com/codedeploy/latest/userguide/reference-appspec-file-structure-hooks.html#appspec-hooks-lambda

https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/automating-updates-to-serverless-apps.html

To meet the requirements of migrating its on-premises Windows and Linux applications to AWS and creating a pilot light DR environment in another AWS Region, the company should use Amazon FSx for NetApp ONTAP for the application storage. Amazon FSx for NetApp ONTAP is a fully managed service that provides highly reliable, scalable, high-performing, and feature-rich file storage built on NetApp’s popular ONTAP file system. FSx for ONTAP supports multiple protocols, including SMB for Windows and NFS for Linux, so the company can access the shared storage from both types of applications. FSx for ONTAP also supports NetApp SnapMirror replication, which enables the company to replicate the storage to the DR Region. NetApp SnapMirror replication is efficient, secure, and incremental, and it preserves the data deduplication and compression benefits of FSx for ONTAP. The company can use automation to launch and configure the EC2 instances in the DR Region and then use NetApp SnapMirror to restore the data from the primary Region.

The other options are not correct because they do not meet the requirements or follow best practices. Using Amazon S3 for the application storage is not a good option because S3 is an object storage service that does not support SMB or NFS protocols natively. The company would need to use additional services or software to mount S3 buckets as file systems, which would add complexity and cost. Using Amazon EBS for the application storage is also not a good option because EBS is a block storage service that does not support SMB or NFS protocols natively. The company would need to set up and manage file servers on EC2 instances to provide shared access to the EBS volumes, which would add overhead and maintenance. Using a Volume Gateway in AWS Storage Gateway for the application storage is not a valid option because Volume Gateway does not support SMB protocol. Volume Gateway only supports iSCSI protocol, which means that only Linux applications can access the shared storage.

1: What is Amazon FSx for NetApp ONTAP? - FSx for ONTAP

2: Amazon FSx for NetApp ONTAP

3: Amazon FSx for NetApp ONTAP | NetApp

4: AWS Announces General Availability of Amazon FSx for NetApp ONTAP

Replicating Data with NetApp SnapMirror - FSx for ONTAP

What Is Amazon S3? - Amazon Simple Storage Service

What Is Amazon Elastic Block Store (Amazon EBS)? - Amazon Elastic Compute Cloud

What Is AWS Storage Gateway? - AWS Storage Gateway