Microsoft SC-200 Microsoft Security Operations Analyst Exam Practice Test

 Table: DeviceFileEvents

 Aggregation function: count()

In Microsoft Defender XDR advanced hunting, data tables such as DeviceFileEvents, DeviceProcessEvents, and CloudAppEvents are used to investigate various types of activities. Since this query aims to investigate an issue related to file activity—specifically identifying when files have been accessed, modified, or created repeatedly—the correct data source table is DeviceFileEvents. This table contains information about file-level activities recorded by Defender for Endpoint sensors, including file path, file name, action type, and user account involved.

The KQL structure shown in the image follows standard hunting query syntax:

DeviceFileEvents

| where Timestamp > ago(2d)

| summarize activityCount = count() by FolderPath, FileName, ActionType, AccountDisplayName

| where activityCount > 5

Here’s why:

The where Timestamp > ago(2d) clause filters results from the last 2 days, a typical timeframe for immediate investigations.

The summarize operator groups events by FolderPath, FileName, ActionType, and AccountDisplayName, then uses count() to determine how many times each file was acted upon.

Finally, where activityCount > 5 filters to show only unusually high-frequency activity, which might indicate suspicious or automated file manipulation.

Microsoft Defender XDR documentation highlights that DeviceFileEvents is the correct schema for file activity investigations, while DeviceProcessEvents focuses on process creation and execution, and CloudAppEvents targets cloud application usage.

Thus, the verified and documented correct completions are:

Table: DeviceFileEvents

Aggregation function: count()

 Internal threat: Modify the access policy settings for the key vault.

 External threat: Modify the Key Vault firewall settings.

For internal threats involving a potential compromise of Fabrikam’s own Azure AD applications, the most direct and least disruptive remediation is to modify the Key Vault access policies (or RBAC assignments, if RBAC for Key Vault data-plane is in use) to immediately remove or reduce the compromised service principal’s permissions (Get/List/Decrypt/Sign/Wrap). Microsoft guidance for Key Vault access emphasizes least privilege and promptly revoking credentials or app permissions when compromise is suspected. Access policies (or data-plane RBAC) govern which identities can access secrets, keys, and certificates; adjusting these stops further data-plane actions by the compromised app. “Resource locks” protect against deletion or configuration changes at the management plane, but they do not remove a compromised identity’s ability to read or use vault objects, so they are not an appropriate first response for this scenario.

For external threats, Microsoft recommends hardening Key Vault firewall and networking: restrict public network access, allow only required IPs, use virtual network rules, and prefer private endpoints. Key Vault includes a built-in firewall for IP and VNet ACLs; tuning these controls reduces exposure to the public internet and blocks unauthorized traffic. NSGs apply to IaaS subnets/nics and don’t directly secure the public Key Vault endpoint. Azure Firewall can add perimeter control, but it is not necessary for remediating a specific Key Vault Defender alert; the most effective and immediate remediation is tightening the Key Vault firewall settings to limit external access pathways.

In Microsoft Sentinel (built on Azure Monitor Logs), analytics and hunting queries are executed within a Log Analytics workspace. To run Sentinel queries for Fabrikam, the tenant must have at least one workspace (with Sentinel enabled) in its subscription to host rules, incidents, hunting queries, and workbooks. Sentinel’s cross-workspace/tenant capability is provided by cross-resource queries in Kusto Query Language (KQL). The key construct for reaching outside the current workspace is the workspace() function, which lets you reference another Log Analytics workspace by name or resource ID—even across subscriptions or tenants when proper permissions (often via Azure Lighthouse or guest access) are in place.

Typical correlation looks like:

union workspace('Fabrikam-WS').SecurityEvent, workspace('Contoso-WS').SecurityEvent | …

Here, workspace() is the required element to bring together data sets from multiple tenants; operators like extend and project only shape columns and do not establish cross-tenant scope. Therefore, to meet the requirements with minimal overhead: Fabrikam needs one workspace to host its Sentinel content, and you use workspace() in your KQL to correlate Contoso and Fabrikam data.

As outlined in Microsoft’s official Defender for Office 365 documentation, this service provides comprehensive protection against threats targeting Microsoft 365 collaboration tools—such as SharePoint Online, OneDrive for Business, and Microsoft Teams. The marketing team uses SharePoint Online for vendor collaboration and has experienced incidents in which vendors uploaded malicious files. Microsoft Defender for Office 365 specifically addresses this scenario through features like Safe Attachments and Safe Links, which automatically scan uploaded or shared files for malware and block access to harmful content.

When a vendor uploads a file to SharePoint Online, Defender for Office 365 inspects the file in real time within a virtual sandbox environment before allowing users to open or share it. If malware is detected, the system quarantines or removes the file and notifies administrators. These detection and remediation capabilities prevent infection propagation, protect sensitive marketing data, and maintain compliance with Contoso’s security posture.

By leveraging Defender for Office 365, Contoso’s marketing team can continue external collaboration safely, ensuring that all uploaded files are scanned and validated before internal access—thereby resolving their specific malware-related issue.

To remediate active attacks automatically once alerts or incidents are detected, Microsoft Sentinel uses playbooks, which are workflows built on Azure Logic Apps. These playbooks can execute remediation actions—such as isolating a machine, blocking an account, or triggering other security control changes—without manual intervention. Microsoft’s documentation clearly states that “playbooks in Microsoft Sentinel are based on workflows built in Azure Logic Apps” and that they can “automate and orchestrate your threat response by using playbooks … run a playbook on-demand or automatically in response to specific alerts or incidents.”

When an analytics rule in Sentinel triggers an alert or incident, you can attach an automation rule which in turn invokes a playbook (i.e. a Logic Apps workflow) to perform the remediation steps. The automation rule defines the trigger conditions and calls the playbook action as part of its response actions.

Let us evaluate other options:

Azure Automation runbooks (Option A) are powerful for scripting in Azure (e.g., PowerShell or Python) and can perform remediation tasks, but they are not the native mechanism within Sentinel for orchestrated, alert-driven response workflows.

Azure Functions (Option C) are serverless compute for custom code, but you would have to build and integrate orchestration logic manually; they are not the out-of-box SOAR component in Sentinel.

Azure Sentinel livestreams (Option D) is not a recognized remediation automation component—it is irrelevant in this context.

Therefore, the correct solution to remediate active attacks (triggering automated actions in response to alerts/incidents with minimal manual effort) is to use Azure Logic Apps (via Sentinel playbooks) as the orchestration engine. Logic Apps are the documented foundation of Sentinel’s automation response capabilities.

According to Microsoft Security Operations documentation, Microsoft Defender for Endpoint is designed to protect endpoint devices—including Windows, macOS, Android, and iOS—against cyberattacks through advanced behavioral analysis, threat intelligence, and automated investigation and remediation. In the given case study, the sales team exclusively uses iOS devices and has previously experienced attacks while exchanging files using third-party applications. These unmanaged file-sharing methods exposed the team to malware, phishing, and data leakage threats.

By implementing Microsoft Defender for Endpoint on iOS, Contoso can apply unified endpoint protection across all mobile devices. Defender for Endpoint’s mobile threat defense (MTD) capabilities detect malicious apps, risky network connections, jailbroken devices, and phishing attempts. It also integrates with Microsoft Intune for compliance enforcement and conditional access—ensuring only secure, compliant devices can access corporate resources. This directly mitigates the security challenges faced by the sales team while minimizing manual investigation effort through automated response.

Therefore, the issue affecting the sales team (mobile device attacks and unsafe file transfers) can be effectively resolved using Microsoft Defender for Endpoint.

To complete the KQL query against the BehaviorAnalytics table, you need to know the exact column name (for example, the Boolean field that flags a new or first-time country for the sign-in). Microsoft’s standard method to discover table schemas and column names is the Logs (Log Analytics) query window. In this pane, the left-hand Schema browser lists all connected tables and, when expanded, shows every column name and data type. Selecting a table (e.g., BehaviorAnalytics) reveals its fields, and the editor provides IntelliSense/autocomplete for columns as you type your KQL, making it straightforward to complete a clause like | where == true.

Security alerts in Azure Security Center (Defender for Cloud), the Azure Activity log, and Azure Advisor do not expose the per-table column schema needed to build KQL filters. Security Center surfaces alerts and recommendations; the Activity log records control-plane operations; and Advisor provides optimization guidance—none of these replace the Logs experience for exploring data schemas.

Therefore, to accurately identify and verify the column required in the where clause for failed sign-ins from a first-time country, you should use the Log Analytics workspace query window, consult the Schema pane for the BehaviorAnalytics table, and leverage the editor’s autocomplete to insert the correct column name.

To monitor Windows Security events from Server1 using the Windows Security Events via AMA connector, the first object you must create is a Data Collection Rule (DCR). With the Azure Monitor Agent (AMA) model used by Microsoft Sentinel, data flow is controlled by DCRs, not by the legacy MMA/OMS workspace settings. A DCR defines what to collect (e.g., the Security event log, specific event IDs or XPath queries), from where (the target machines or machine groups), and where to send it (the Sentinel/Log Analytics workspace). After creating the DCR, you associate it with Server1 (Arc-enabled), and the connector will begin streaming Security events to your Sentinel workspace. Creating a Sentinel scheduled rule or an automation rule does not enable collection; those features act after data is already ingested. Event Grid topics are unrelated to Windows event collection. Therefore, the correct first step for meeting the Sentinel requirement to monitor Server1’s Security log via AMA is to create a DCR, then assign it to Server1 and the Sentinel workspace.

The Defender for Cloud requirement states:

“The members of Group1 must be able to enable Defender for Cloud plans and apply regulatory compliance initiatives.”

According to Microsoft Defender for Cloud’s RBAC documentation, the Security Assessment Contributor role provides permissions to:

Enable and configure Defender plans,

Manage security policies and initiatives,

View and edit recommendations and compliance results.

This role gives enough permissions to perform Defender configuration tasks but follows the principle of least privilege, unlike broader roles like Owner or Contributor, which grant excessive rights.

✅ Answer for Question 9: C. Security Assessment Contributor

The Sentinel requirement states:

“Ensure that multiple alerts generated by rulequery1 in response to a single user launching Azure Cloud Shell multiple times are consolidated as a single incident.”

This behavior is controlled by the event grouping setting in the analytics rule configuration.

Microsoft Sentinel documentation explains:

“Event grouping determines how alerts generated from the same rule are grouped into incidents. Selecting ‘Group all alerts triggered by this rule into a single incident’ allows all related alerts to be combined.”

Hence, configuring event grouping ensures that all alerts from rulequery1 related to the same user are consolidated into one incident, satisfying the requirement.

✅ Answer for Question 10: C. event grouping

Microsoft Sentinel playbooks can be triggered by different types of events—alerts, incidents, or entities. Since the requirement specifies “incidents generated by rulequery1” and asks for automatic processing of those incidents, the correct approach is to use a playbook with an incident trigger.

According to Microsoft Sentinel automation documentation:

“When you want automation to start after an incident is created or updated, use the incident trigger. This allows you to automate workflows such as closing incidents, adding comments, or sending notifications.”

This trigger type works with automation rules that specify when and how to execute playbooks based on incident state or severity. Using a playbook with an incident trigger meets the need for post-incident automation (e.g., auto-closing incidents if they match certain conditions).

✅ Answer for Question 8: A. a playbook with an incident trigger

In Microsoft Sentinel, Hunting queries are used to proactively search for threats and anomalies across collected security data. The requirement states that HuntingQuery1 must run automatically when the Hunting page of Microsoft Sentinel is accessed. According to Microsoft’s official Sentinel documentation, this behavior is achieved by adding the hunting query to “Favorites.”

When a query is marked as a favorite in the Sentinel Hunting blade, Sentinel automatically runs it every time the Hunting page is opened. This provides updated results without the need for manual execution, ensuring analysts always see the most current data for that query. This configuration also adheres to the organization’s requirement to minimize administrative effort, since it eliminates the need for scheduling, automation, or manual refresh actions.

Other options do not meet this functional requirement:

A. Add to a livestream is used to monitor near-real-time data streams, not to auto-run queries upon accessing the Hunting page.

B. Create a watchlist is used for referencing static external data sets (like IPs, users, or devices) inside KQL queries, not for automating hunting query execution.

C. Create an automation rule applies to incident management workflows, not hunting query execution.

Therefore, as per Microsoft Sentinel’s hunting documentation and best practices, the correct and verified answer is:

D. Add HuntingQuery1 to favorites.

To have a Microsoft Sentinel workbook pull live data from an external web service, you use the workbook’s built-in JSON data source. Workbooks can query REST endpoints that return JSON and render results dynamically in visuals. Because the Azure portal (where the workbook runs) is a different origin than your on-prem/public Webapp1, browsers will block these cross-origin requests unless the endpoint explicitly allows them. Therefore, the external service must enable CORS to permit the portal’s origin to fetch the JSON. This aligns with Microsoft guidance that Workbooks can query HTTP/HTTPS JSON endpoints and that external endpoints must allow cross-origin requests for client-side calls from the Azure portal. Enforcing CORS on Webapp1 satisfies the security model while enabling the workbook to retrieve data at view time—meeting the requirement to “dynamically retrieve data from Webapp1.” Options like “custom endpoint” or “custom resource provider” aren’t needed here, and enabling SOP or only enforcing TLS 1.2 wouldn’t address the browser’s cross-origin policy blocking the call.

For a near-real-time (NRT) analytics rule that detects sign-ins by a designated break-glass account, the most direct and performant pattern is to filter SigninLogs by joining to a Microsoft Sentinel watchlist that contains the protected account(s). Sentinel exposes watchlists to KQL through the helper function _GetWatchlist('<watchlist-name>'), which returns a table with standard columns (including SearchKey) plus any custom columns you imported. Using join kind=inner ensures the result set includes only those SigninLogs rows whose UserPrincipalName matches an entry in the watchlist—ideal for alerting on a high-value account without post-filtering.

The completed query is:

SigninLogs | join kind=inner (_GetWatchlist('breakglass_account')) on $left.UserPrincipalName == $right.SearchKey

This approach satisfies the requirement to implement an NRT rule for the break-glass account because:

NRT rules support KQL with joins and watchlists and are optimized for rapid evaluation over fresh data.

Using a watchlist lets SecOps adjust monitored accounts without editing the rule—minimizing administrative effort and aligning with least-privilege operations (no extra permissions beyond watchlist management).

The inner join pattern reduces noise by returning only matched events, which are then turned into alerts/incidents by the NRT rule.

Thus, select join and GetWatchlist, and join UserPrincipalName to the watchlist’s SearchKey.

To monitor and detect higher-than-normal volumes of password resets, you need to gather password reset event data both from Azure Active Directory (cloud identities) and from on-premises Active Directory (domain accounts). Microsoft’s official Defender XDR and Sentinel integration guidance describes that:

Azure AD Password Protection enforces and monitors password policies in both cloud and hybrid environments. It can detect weak, commonly used, or compromised passwords and logs related password change/reset activities. Deploying Azure AD Password Protection extends password reset visibility to on-premises domain controllers through the Password Protection proxy and DC agent. This makes it the correct choice for implementing monitoring at the identity environment level.

In Microsoft Sentinel, to ingest and analyze password reset activities from on-premises servers (e.g., domain controllers), you must use the Windows Security Events via AMA connector. This connector collects Event ID 4723 (password change), 4724 (password reset), and related security logs directly from Windows Servers into the Sentinel Log Analytics workspace through the Azure Monitor Agent (AMA). Once the events are available in Sentinel, they can be correlated with other identity or behavioral analytics to detect abnormal reset volumes or potential compromise attempts.

The other options are not suitable:

Microsoft Defender for Identity focuses on identity compromise detection, not specifically on password reset volume monitoring.

Smart lockout protects against brute-force sign-in attempts but doesn’t generate detailed reset event telemetry.

Microsoft security rule and UEBA are higher-level analytic configurations, not data ingestion mechanisms.

Therefore, to meet the Sentinel requirements for monitoring password reset anomalies:

✅ Implement in the identity environment: Azure AD Password Protection

✅ Configure in Microsoft Sentinel: The Windows Security Events via AMA connector

The requirement is to enable Microsoft Defender for Servers Plan 2 on all Azure VMs while excluding Server2 from agentless scanning. Defender for Cloud provides a built-in mechanism to exclude specific machines from agentless scanning based on resource tags. The process is: assign a distinct tag name:value to the VM you want to exclude (Server2), and then, in Defender for Cloud’s Agentless scanning for machines settings, specify that tag pair under exclusions. Defender’s continuous discovery honors these exclusions and skips any VM that matches the configured tag. This approach aligns with the business requirement of least privilege and minimal administrative effort: it avoids broad configuration changes, requires no extensions on the VM, and is reversible by simply removing or changing the tag. The Microsoft Antimalware extension and Automanage configuration are unrelated to agentless scanning behavior, and a resource lock would only prevent modifications/deletions, not scanning. Therefore, to meet the Defender for Cloud requirement precisely, configure an Azure resource tag on Server2 and reference that tag in the agentless scanning exclusion settings.

The case study requires:

“Ensure that the Group1 members can create and edit playbooks.”

In Microsoft Sentinel, the ability to create, edit, and assign playbooks is granted by the Microsoft Sentinel Automation Contributor role.

This role allows users to:

Create and manage automation rules,

Create and edit playbooks (Logic Apps) in the connected subscription,

Associate playbooks with Sentinel incidents or alerts.

By contrast:

Logic App Contributor allows Logic App creation but doesn’t include Sentinel-level integration permissions.

Automation Operator can run playbooks but not edit or create them.

Sentinel Playbook Operator can execute playbooks but cannot modify or assign them.

✅ Answer for Question 11: A. Microsoft Sentinel Automation Contributor

In Microsoft Sentinel’s Advanced Security Information Model (ASIM), DNS queries are normalized through the Im_Dns parser, which unifies DNS telemetry from multiple sources (Infoblox, Windows DNS, Azure Firewall DNS proxy, etc.). Microsoft guidance states that when you need broad compatibility and want to “use built-in ASIM parsers whenever possible,” you should call the generic Im_Dns() parser. To minimize overhead, ASIM provides a pack parameter that restricts the parser to a specific content pack (vendor/source) so it won’t iterate through all available source parsers under the hood. For Infoblox NIOS, you pass the Infoblox pack via the pack parameter, which limits parsing to the Infoblox implementation and reduces query cost/latency while keeping the query portable across environments.

Putting it together, the recommended pattern is:

Im_Dns(pack="InfobloxNIOS")

| where DnsResponseCodeName == "NXDOMAIN"

| summarize count()

This approach satisfies all requirements:

Uses built-in ASIM (Im_Dns).

Minimizes query overhead (uses pack to limit parsing to Infoblox).

Targets NXDOMAIN responses for counting DNS request failures from Infoblox1.

To restrict cloud apps running on CLIENT1 (a Windows 10 endpoint) in compliance with Microsoft Defender for Endpoint (MDE) requirements, you must integrate Microsoft Defender for Endpoint with Microsoft Defender for Cloud Apps (formerly Cloud App Security) using Cloud Discovery. This integration enables the blocking of unsanctioned cloud apps through the endpoint’s network protection capabilities.

According to Microsoft Defender for Cloud Apps documentation, Cloud Discovery uses traffic data from Defender for Endpoint to identify and manage the use of shadow IT. The relevant steps include:

1️⃣ Enable advanced features in Microsoft Defender for Endpoint (M365 Defender portal → Settings → Endpoints → Advanced features).

You must enable the following advanced features:

Microsoft Defender for Cloud Apps integration

Network protection (in block mode)

Custom network indicators (if applicable)These options allow Defender for Endpoint to share telemetry and enforce app restrictions received from Defender for Cloud Apps.

2️⃣ Configure Cloud Discovery settings in Microsoft Defender for Cloud Apps.

In the Defender for Cloud Apps portal, Cloud Discovery must be configured to receive continuous reports from Defender for Endpoint devices. Within these settings, you define sanctioned and unsanctioned applications. Once an app is marked as unsanctioned, Defender for Endpoint enforces blocking on all onboarded devices (like CLIENT1).

This two-part configuration ensures that MDE enforces the blocking of unsanctioned cloud applications discovered through Cloud App Security telemetry, fulfilling the business requirement that “All Cloud App Security unsanctioned apps must be blocked on the Windows 10 computers by using Microsoft Defender for Endpoint.”

According to Microsoft Defender for Cloud (formerly Azure Security Center) documentation, when integrating servers and virtual machines for security monitoring, the Defender for Cloud data is stored and analyzed in a Log Analytics workspace. Microsoft best practices recommend using an existing workspace when one is already deployed for centralized log collection—especially if it already gathers telemetry from Azure and on-premises resources.

In this case, the existing workspace LA1 already “contains logs and metrics collected from all Azure resources and on-premises servers.” This satisfies the business requirement of “all servers must send logs to the same Log Analytics workspace.” Creating a new workspace would violate the cost-minimization and centralization requirements. Therefore, LA1 is the correct workspace to use.

For the Windows security events collection setting, Defender for Cloud offers three levels:

Minimal – collects essential security events only (insufficient for comprehensive monitoring).

Common – collects a balanced set of security-related events (such as account logon, privilege use, and object access), recommended by Microsoft for most environments.

All Events – collects every possible security event, which increases data ingestion cost and is typically used only for high-security or regulated environments.

Because Litware Inc. must minimize cost while ensuring a full audit trail of user activities, the Common level provides the optimal balance of visibility and cost efficiency.

Therefore, based on Microsoft Defender for Cloud configuration guidelines:

✅ Log Analytics workspace to use: LA1

✅ Windows security events to collect: Common

According to Microsoft’s official Defender for Identity deployment documentation, setting up Microsoft Defender for Identity (MDI) on an on-premises domain controller (DC) such as DC1 requires a specific sequence of steps. MDI monitors and protects Active Directory from advanced threats by using sensors installed directly on domain controllers.

The correct sequence is as follows:

1️⃣ Provide global administrator credentials to the Azure AD tenant.

Microsoft Defender for Identity is created and managed in the Microsoft 365 Defender portal or Microsoft Security portal, which requires global administrator permissions to provision the MDI instance and connect it to the Azure AD tenant.

2️⃣ Create an instance of Microsoft Defender for Identity.

You must create an MDI instance in the portal before deploying any sensors. This instance defines your Defender for Identity workspace and generates the configuration package that sensors will later use to connect to the cloud service.

3️⃣ Provide domain administrator credentials to the on-premises Active Directory domain.

Domain admin credentials are required for the sensor installation because the sensor must access the domain controller’s security logs, event logs, and directory services to monitor authentication traffic and suspicious activities.

4️⃣ Install the sensor on DC1.

Since DC1 is a domain controller connected directly to the internet, the standard sensor (not the standalone sensor) is installed directly on it. The standalone sensor is used only when you do not want to install the sensor directly on a DC. The sensor automatically connects to the created MDI instance and begins collecting telemetry for detection.

This sequence aligns with Microsoft Defender for Identity deployment guidance, ensuring secure onboarding of DC1 while maintaining the principle of least privilege and meeting the business requirement to protect all domain controllers.

✅ Final Order:

Provide global administrator credentials →

Create instance of Microsoft Defender for Identity →

Provide domain administrator credentials →

Install the sensor on DC1.

To show labeled files from Windows 10 endpoints in the Azure Information Protection – Data discovery dashboard, you must first enable the built-in integration between Microsoft Defender for Endpoint and Azure Information Protection (AIP). This is turned on in the Microsoft Defender Security Center under Settings → Advanced features. When enabled, Defender for Endpoint inventories sensitivity labels seen on files across managed Windows devices and streams that telemetry to the AIP Data discovery experience, providing visibility into where labeled data resides on endpoints. Scanner clusters and content scan jobs in AIP are intended for on-premises repositories (file shares/SharePoint servers), not for endpoint discovery. Device health/compliance reports do not surface or forward label inventory to AIP. Therefore, the first configuration step is enabling the AIP integration advanced feature in Defender for Endpoint so labeled files on Windows clients appear in the AIP Data discovery dashboard.

The problem described in the case study states that “Cloud App Security frequently generates false positive alerts when users connect to both offices simultaneously.” This behavior is commonly associated with the “Impossible travel” anomaly detection policy in Microsoft Defender for Cloud Apps.

According to Microsoft documentation, the “Impossible travel” policy detects when a user signs in from two locations that are geographically distant within an unrealistic timeframe. However, in multi-office environments (such as Boston and Seattle) or when VPN connections are used, this policy can frequently trigger false positives, since it may misinterpret legitimate connections as anomalous.

Microsoft recommends adjusting the impossible travel detection policy to account for trusted IP ranges, known locations, or VPN endpoints to reduce false alerts. Specifically, administrators can modify the policy’s sensitivity, include the organization’s office IP addresses as trusted, and exclude known network ranges.

This approach directly aligns with the case study’s scenario and satisfies the Defender for Cloud Apps requirement to reduce false positives while maintaining user anomaly detection accuracy.

✅ Final Answer for Question 3: D. Impossible travel

To integrate Microsoft Defender for Cloud Apps (MCAS) with Microsoft Sentinel, Microsoft’s official SecOps and Sentinel documentation specifies a two-step configuration process.

In the Defender for Cloud Apps portal – You add a security extension to enable integration with external SIEM platforms. This action allows MCAS to forward its alerts, activities, and discovered app telemetry to other Microsoft or third-party security platforms. By adding the security extension, Defender for Cloud Apps is authorized to send data streams and alerts to Microsoft Sentinel through a supported API connection.

In Microsoft Sentinel (Azure portal) – You then add a data connector. Data connectors in Sentinel are predefined integration pipelines that bring in telemetry from Microsoft or external security solutions. The Microsoft Defender for Cloud Apps connector specifically ingests MCAS alerts and audit logs into Sentinel, where they can be correlated with other Microsoft Defender XDR signals, enabling unified detection and investigation across identity, endpoint, and cloud layers.

This integration approach adheres to Microsoft’s principle of minimizing administrative effort by using native connectors rather than custom ingestion or log collector configurations. Once connected, Sentinel automatically normalizes MCAS alerts into its SecurityAlert and CloudAppEvents tables for rule creation, playbook automation, and incident correlation.

Therefore, the verified correct configuration is:

Defender for Cloud Apps: Add a security extension

Sentinel: Add a data connector

Azure Sentinel “playbooks” are Azure Logic Apps. Granting the minimal permissions to configure (create/edit) playbooks requires the Logic App Contributor role on the resource group where the playbooks reside. This satisfies the business requirement to use least privilege and specifically enables admin1 to design, modify, and manage Logic Apps that Sentinel automation rules or analytics rules will invoke. Roles like Automation Operator or Automation Runbook Operator apply to Azure Automation, not Logic Apps, and therefore don’t allow creating or editing Sentinel playbooks. Azure Sentinel Contributor allows managing Sentinel resources (incidents, analytics rules, workbooks) but, by itself, does not grant permissions to author Logic Apps. Assigning Logic App Contributor provides precisely what is needed to configure Sentinel playbooks without unnecessary broader permissions.

The test analytics rule must generate alerts for inbound Office 365 access by several test users and group those alerts into separate incidents—one per user. In Azure Sentinel, incident grouping by entity depends on the rule’s Entity mapping. When you create a scheduled analytics rule, under Set rule logic you map columns from your query to entities like Account, IP, or Host. Once mapped, you can configure Event grouping so alerts with the same entity value (e.g., the same Account) are automatically grouped into a single incident. Turning suppression on/off or changing severity/tactics doesn’t influence entity-based incident grouping. Therefore, to ensure “one incident per test user account,” you must map the Account entity (and any other relevant entities) in Set rule logic, then enable grouping by that entity—fulfilling the Sentinel requirement.QUESTION NO: 3 HOTSPOT

You need to create the analytics rule to meet the Azure Sentinel requirements.

What should you do? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Answer:

According to Microsoft Security Operations (SecOps) and Azure Sentinel documentation, when you need to create an analytics rule that executes a custom KQL query and automatically initiates a playbook, the correct configuration is to create a Scheduled rule and ensure the playbook includes a trigger.

Here’s why:

A Scheduled analytics rule in Microsoft Sentinel (Microsoft Defender XDR portal) is designed for running custom KQL queries at defined intervals (for example, every hour or every few minutes) to detect specific patterns of suspicious activity. When the rule’s conditions are met, Sentinel generates alerts that can automatically trigger a playbook for response and automation.

A playbook in Sentinel is an Azure Logic App that automates responses to incidents or alerts. To connect a playbook to an analytics rule, it must include a trigger—specifically, the “Microsoft Sentinel Alert” or “Incident trigger.” This allows the rule to start the playbook automatically when the defined condition is met.

The other options are incorrect because:

Fusion rules are built-in and use Microsoft’s machine learning to correlate signals automatically; they can’t be used for custom queries.

Microsoft incident creation rules are also built-in and handle alert-to-incident grouping logic, not custom query execution.

A service principal would be needed for permissions (e.g., admin1 configuring playbooks), but not inside the playbook itself.

Diagnostics settings apply to log collection and retention, not rule automation.

Therefore, based on Microsoft Sentinel best practices and documentation:

✅ Create the rule of type: Scheduled

✅ Configure the playbook to include: A trigger

To block unsanctioned cloud apps on Windows 10 endpoints with Microsoft Defender for Endpoint and Microsoft Defender for Cloud Apps (formerly Cloud App Security), you must enable and configure the product integration on both sides. First, in Microsoft Defender Security Center → Settings → Advanced features, turn on the Microsoft Defender for Cloud Apps integration (and ensure network protection prerequisites are met). This allows Defender for Endpoint to receive the unsanctioned app list and enforce endpoint-based blocking when users on CLIENT1 attempt to access those apps via the browser or client.

Second, in Defender for Cloud Apps → Settings → Cloud Discovery, configure the Microsoft Defender for Endpoint integration and enable Block unsanctioned apps. In Cloud Discovery, apps are discovered, assessed, and can be tagged as Unsanctioned. Once the MDE integration is enabled, that tag is exported to endpoints, which then enforce blocking based on the tenant’s app catalog and policies.

Options A (Onboarding settings) are for enrolling devices and do not control app blocking behavior. B (Anomaly detection policies) govern behavioral detections (e.g., impossible travel, anonymous IP) and are unrelated to endpoint enforcement of app access. Therefore, the two configurations you must modify to meet the requirement “block unsanctioned apps on Windows 10 computers by using Microsoft Defender for Endpoint” are C. Advanced features in Microsoft Defender Security Center and D. Cloud Discovery settings in Cloud App Security.

Comprehensive and Detailed Explanation with all Microsoft Security Operations (SecOps) documents: =

To monitor Linux virtual machines in AWS using Azure Defender (Microsoft Defender for Cloud), you must onboard those VMs into Azure’s management plane. The recommended and supported approach is via Azure Arc.

Azure Arc enables Azure Defender to extend its security monitoring and policies to non-Azure machines (including AWS and on-premises). Once onboarded through Azure Arc, the Log Analytics agent (MMA/AMA) can be automatically provisioned, allowing Defender for Cloud to collect and analyze security data.

Microsoft documentation states:

“To monitor machines outside Azure (on-premises or in other clouds like AWS or GCP), onboard them to Azure using Azure Arc. Azure Defender will then auto-provision the required agent and begin monitoring.”

Thus, enabling Azure Arc and onboarding the AWS VMs meets the goal.

✅ Correct answer: A. Yes

In Microsoft Defender for Endpoint, Live Response provides an interactive remote shell for investigating a device directly from the Microsoft Defender portal. It allows security analysts to run commands, collect data, inspect processes, and perform incident response tasks without manually logging into the endpoint.

For this scenario, you must:

Identify all active network connections.

Identify all running processes.

Retrieve login history.

All of these can be achieved efficiently through a Live Response session. Once connected, you can use built-in commands such as:

netstat to view active network connections,

ps to list running processes,

cat or less to review log files containing login history.

According to Microsoft Defender for Endpoint documentation, “Live response enables analysts to perform in-depth investigation on a device remotely to collect forensic data, run scripts, and remediate threats.” It is the least administrative-intensive way to gather this information compared to collecting an investigation package, which is more time-consuming and primarily for offline forensic analysis.

Option A (disable authenticated telemetry) is unrelated to investigation tasks.

Option B (enable unsigned script execution) is only needed for running custom scripts, not built-in commands.

Option C (collect investigation package) gathers data for offline review and does not allow interactive analysis.

Option D (initiate live response) gives immediate, interactive insight into processes, connections, and logs — satisfying all requirements with minimal effort.

✅ Therefore, the correct first step is to initiate a live response session on Device1.

When modifying an existing Azure Sentinel playbook (Logic App) to send emails to a dynamic recipient, such as the resource owner, you must make the email destination configurable. This is achieved by adding a parameter in the Logic App.

You then modify the action (the “Send an email” step) to use this parameter as the recipient field instead of a static distribution list. This enables flexibility, allowing the playbook to adapt dynamically based on alert context or Sentinel data fields passed into it.

In Microsoft Sentinel documentation, the best practice for dynamic logic app responses is:

“Use parameters to pass entity information (such as Account, Host, or Owner) from Sentinel alerts into playbooks for dynamic action execution.”

Hence, the verified answer is D. Add a parameter and modify the action.

To run a PowerShell script when a suspicious-IP access alert is raised for Storage, use Workflow automation in Defender for Cloud to trigger a Logic App whenever a matching security alert occurs. The Logic App should use the Azure Security Center (Defender for Cloud) alert trigger, and from there you can call an Automation Runbook (PowerShell) or another action to execute your script. A manual or HTTP trigger isn’t needed because the flow must start automatically from the security alert, and an AAD app registration is not required for the basic alert-triggered flow.

In Microsoft 365 Defender advanced hunting, if you want to automatically receive alerts based on a KQL query—such as detecting when a process disables System Restore—you must convert that query into a custom detection rule. According to Microsoft’s official documentation, custom detection rules “run hunting queries on a schedule and create alerts and incidents when results are found.”

In order for the detection rule to function properly and correlate results across devices and incidents, the query must output DeviceId and ReportId. These fields are mandatory for any advanced hunting query that you want to convert into a detection rule because they uniquely identify the device and event instance. Without them, the rule cannot properly generate correlated alerts.

Therefore:

Create a detection rule (A) – ensures the query runs automatically and alerts are generated.

Add DeviceId and ReportId (E) – required for detection rule creation and accurate device/event correlation.

Other options are incorrect:

Suppression rule (B) filters alerts, not generate them.

Order by Timestamp (C) is optional for display, not alerting.

DeviceNetworkEvents (D) is unrelated to this process query.

To correlate malicious email attachments with endpoints, Microsoft Defender data schemas expose attachment metadata in EmailAttachmentInfo (including the SHA256 hash) and endpoint file activity in DeviceFileEvents (which also records SHA256 for observed files). The recommended investigation pattern is: (1) filter email telemetry to the suspected sender and keep only attachments with a populated hash; (2) join that result with endpoint file events on the SHA256 hash to find devices where an identical file (by cryptographic hash) was seen. In KQL, isnotempty(SHA256) ensures you only pass attachments with a valid hash to the join, and join ... on SHA256 performs an exact-match correlation across datasets. This method aligns with Defender’s guidance to use hash-based correlation as the most reliable way to match artifacts across different security workloads (email vs. endpoint), since filenames and paths can change, but a cryptographic hash uniquely identifies file content. The final project selects operational fields for response—timestamps, file name and hash, device identifiers/names, message IDs, and sender/recipient—so the SOC can quickly pivot to the impacted devices and the original email that delivered the file.

Microsoft Defender XDR’s Automatic attack disruption requires EDR in block mode to be enabled. This feature allows Defender for Endpoint to block or contain malicious activities, even when the primary antivirus engine fails to detect or block them initially.

According to Microsoft Defender documentation, attack disruption relies on real-time EDR response capabilities and EDR in block mode to isolate compromised users or devices automatically. Without EDR in block mode, Defender can only alert — not stop — ongoing attacks.

Enabling EDR in block mode integrates the EDR layer with Microsoft Defender Antivirus to automatically contain lateral movement and credential theft activities. While Tamper Protection, Live Response, and Standard Discovery are valuable, they do not directly enable automatic attack disruption.

✅ Correct Answer: A. Set Enable EDR in block mode to On

To combine Microsoft Graph activity logs with Microsoft Entra ID Protection risky users, while minimizing data volume, you use the lookup kind=leftouter operator.

Microsoft documentation explains:

“Use lookup (instead of join) when you need to enrich a large dataset with a small lookup table to minimize data returned.”

MicrosoftGraphActivityLogs provides user activity records.

AADRiskyUsers lists users detected as risky by Entra ID Protection.

A leftouter lookup ensures all Microsoft Graph activity entries are kept, while only matching risky user info is appended — reducing output volume compared to a full join.

✅ Correct Answer: A. MicrosoftGraphActivityLogs lookup kind=leftouter AADRiskyUsers on $left.UserId == $right.Id

Automated Investigation and Response (AIR) automates investigation and remediation actions for alerts that Defender already detects: it triages alerts, runs investigation playbooks, and can execute remediation (quarantine files, terminate processes, remove persistence) based on the investigation outcome. AIR is powerful for reducing analyst load and quickly remediating detected threats. However, AIR only runs in response to detections/alerts it receives—if the third-party AV completely misses an artifact and no EDR/behavioral detection generates an alert, AIR will not be triggered. In contrast, EDR in block mode is specifically built to catch post-breach detections that the primary AV missed and to remediate them. Therefore, enabling AIR alone does not guarantee protection from artifacts missed by the third-party antivirus; AIR helps remediate once a detection exists but does not itself create the missed detection coverage that EDR in block mode provides.

When onboarding Google Cloud Platform (GCP) to Microsoft Defender for Cloud using the native cloud connector, the integration process is performed within GCP to allow Defender for Cloud to continuously monitor all existing and future GCP projects under the organization.

According to Microsoft Defender for Cloud’s official onboarding documentation for GCP, the connector setup requires the creation of a management project in GCP. This project acts as a central control point for all future onboarding operations. In that project, you create a custom IAM role that defines the minimum required permissions for Defender for Cloud to access security posture, asset inventory, and threat detection data from GCP resources.

This ensures least-privilege access and allows automatic onboarding of all new GCP projects under the same organization (GCP1).

Microsoft provides a deployment script that you execute from the GCP Cloud Shell. Running the script there automates:

Creation of the management project

Assignment of the custom role

Configuration of the service account and necessary API permissions

Establishment of the continuous connector between GCP and Defender for Cloud

Running it in Azure Cloud Shell would not have the required GCP SDK environment or permissions to modify GCP IAM and projects, hence GCP Cloud Shell is required.

✅ Final Answers:

Create: A management project and a custom role

By: Running a script in GCP Cloud Shell

 Policy template type: Access policy

 Filter based on: IP address tag

In Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security), policies are used to detect, alert, and control access or activity patterns across cloud applications.

To detect connections originating from a botnet network, you need a policy that evaluates real-time access conditions such as the user’s IP address, device, or location at the time of the connection attempt. This is achieved through an Access policy, which controls and monitors session access to cloud apps using Conditional Access App Control.

Microsoft documentation specifies that Access policies can filter based on IP address ranges, tags, or risk levels. The “IP address tag” is particularly used to classify addresses into categories like “Risky,” “Anonymous proxy,” “Botnet,” etc. Microsoft’s built-in IP address tagging capability recognizes malicious or suspicious sources, including known botnet IPs.

Activity policies monitor in-app user actions such as file downloads, sharing, or admin operations—not the connection origin.

Anomaly detection policies rely on behavioral analytics and machine learning, not static IP classifications, and cannot explicitly target botnet IPs.

Therefore, to meet the requirement of detecting connections to Microsoft 365 apps from botnet networks, you must configure an Access policy that filters based on the IP address tag set to “Botnet.”

In the Microsoft Purview compliance portal, when you investigate aggregated Data Loss Prevention (DLP) alerts, the Events tab shows all the individual policy matches that were aggregated into the alert. Each event includes details such as:

Impacted users (entities)

Files, emails, or activities involved

The policy and rule that triggered the detection

The Details tab provides general alert info, and Sensitive Info Types lists matched data classifications—but impacted entities (users, files, etc.) are shown in the Events tab.

✅ Answer: D. the Events tab of the alert

When you deploy the ASIM unifying parser for DNS, you should query the normalized schema via the function imDns() (ASIM convention: im). To maximize performance, pass filtering arguments directly to the parser so the function prunes data early at source, rather than piping a large dataset into subsequent where filters. ASIM DNS supports parameters such as starttime, endtime, and responsecodename (for example, NXDOMAIN). Therefore, using imDns(starttime=ago(1d), responsecodename='NXDOMAIN') limits ingestion to the last 24 hours and only events whose ResponseCodeName equals NXDOMAIN.

Once normalized rows are returned, use summarize with bin() to aggregate efficiently. The ASIM DNS schema exposes the client address as SrcIpAddr; grouping by this field and bin(TimeGenerated, 15m) produces per–source-IP counts in 15-minute buckets. The final query:

imDns(starttime=ago(1d), responsecodename='NXDOMAIN')

| summarize count() by SrcIpAddr, bin(TimeGenerated, 15m)

meets all requirements: it lists all DNS events with NXDOMAIN in the last 24 hours and aggregates them by source IP in 15-minute intervals, using ASIM’s parameterized parser to achieve optimal performance.

Step 1: From Logic App Designer, create a logic app.

Create a logic app and define when it should automatically run

1. From Defender for Cloud's sidebar, select Workflow automation.

2. To define a new workflow, click Add workflow automation. The options pane for your new automation opens.

Here you can enter:

A name and description for the automation.

The triggers that will initiate this automatic workflow. For example, you might want your Logic App to run when a security alert that contains "SQL" is generated.

The Logic App that will run when your trigger conditions are met.

3. From the Actions section, select visit the Logic Apps page to begin the Logic App creation process.

4. Etc.

Step 2: From Logic App Designer, run a trigger.

Manually trigger a Logic App

You can also run Logic Apps manually when viewing any security alert or recommendation.

Step 3: From Workflow automation in Defender for cloud, add a workflow automation.

Configure workflow automation at scale using the supplied policies

Automating your organization's monitoring and incident response processes can greatly improve the time it takes to investigate and mitigate security incidents.

Use livestream to run a specific query constantly, presenting results as they come in.

To ingest security logs from Linux virtual machines into Microsoft Sentinel efficiently, Microsoft recommends using the Common Event Format (CEF) connector.

The CEF connector allows Linux machines to send logs in a structured, normalized format via Syslog, minimizing custom parsing in Sentinel. The CEF schema is widely adopted by SIEM and security products, ensuring compatibility and simplified analytics rule creation.

How it works:

The Linux VMs send logs to a local Syslog daemon.

The Syslog daemon forwards the logs (in CEF format) to the Log Analytics workspace connected to Microsoft Sentinel.

Sentinel automatically maps CEF fields, minimizing parsing and normalization effort.

Why not the others:

REST API integration: Requires custom scripting and parsing — high admin effort.

Syslog connector: Sends raw logs that need additional parsing and normalization.

Log Analytics Data Collector API: Used for custom ingestion scenarios, not scalable for 100 VMs.

✅ Correct Answer: D. a Common Event Format (CEF) connector

msticpy is a library for InfoSec investigation and hunting in Jupyter Notebooks. It includes functionality to: query log data from multiple sources. enrich the data with Threat Intelligence, geolocations and Azure resource data. extract Indicators of Activity (IoA) from logs and unpack encoded data.

MSTICPy reduces the amount of code that customers need to write for Microsoft Sentinel, and provides:

Data query capabilities, against Microsoft Sentinel tables, Microsoft Defender for Endpoint, Splunk, and other data sources.

Threat intelligence lookups with TI providers, such as VirusTotal and AlienVault OTX.

Enrichment functions like geolocation of IP addresses, Indicator of Compromise (IoC) extraction, and WhoIs lookups.

Visualization tools using event timelines, process trees, and geo mapping.

Advanced analyses, such as time series decomposition, anomaly detection, and clustering.

In Microsoft Sentinel, a bookmark is a saved record of an event or query result that an analyst can use to support investigations. Bookmarks are typically created during proactive threat hunting or investigation activities to capture and retain evidence for later correlation or incident enrichment.

According to Microsoft’s Sentinel documentation, the Hunting blade provides a workspace where analysts can run Kusto Query Language (KQL) queries across data sources to identify anomalies or suspicious activity. When a suspicious event or pattern is found, the analyst can create a bookmark directly from the Hunting blade. This bookmark stores query details, time range, and contextual information for use in later investigations.

Once the bookmark exists, it can be associated with a specific incident within Sentinel. This association occurs through the Incident blade, where analysts manage alerts, incidents, and investigation details. From the Incident blade, you can attach or link existing bookmarks to provide additional evidence or context, enriching the investigation record and improving traceability within the incident management lifecycle.

In summary:

Hunting blade → Used to create bookmarks from proactive searches or hunting queries.

Incident blade → Used to associate bookmarks with ongoing incidents for investigation correlation.

Therefore, the correct sequence is:

Create a bookmark using the Hunting blade, and associate it with the incident using the Incident blade.

Alert policies let you categorize the alerts that are triggered by a policy, apply the policy to all users in your organization, set a threshold level for when an alert is triggered, and decide whether to receive email notifications when alerts are triggered.

Default alert policies include:

Unusual external user file activity - Generates an alert when an unusually large number of activities are performed on files in SharePoint or OneDrive by users outside of your organization. This includes activities such as accessing files, downloading files, and deleting files. This policy has a High severity setting.

To integrate Microsoft Defender for Cloud with Azure DevOps (AzDO1) for detecting secrets exposed in pipelines, you must connect the two platforms using Microsoft’s built-in integration flow that enables Defender for Cloud to scan repositories and pipelines for vulnerabilities and sensitive data exposure.

Here’s the correct configuration process:

In the Defender for Cloud portal, you first need to add an environment that represents your Azure DevOps organization.

This step connects Defender for Cloud to the DevOps service, allowing it to monitor repositories, build pipelines, and artifacts.

Navigate to Defender for Cloud → Environment settings → Add environment → Azure DevOps.

You’ll then authenticate your Azure DevOps organization (AzDO1) to allow Defender for Cloud to scan your pipelines.

This setup enables Defender for DevOps, a capability within Defender for Cloud, to detect exposed secrets, insecure dependencies, and misconfigurations directly from pipeline activities.

Next, within Azure DevOps (AzDO1), install the Microsoft Defender for DevOps Security Scanner extension from the Azure DevOps Marketplace.

This extension integrates the Defender for Cloud scanning engine into the Azure DevOps pipeline process and enables automatic scanning for:

Hardcoded secrets in YAML pipelines,

Vulnerability findings in open-source dependencies, and

Infrastructure-as-code misconfigurations (for example, ARM, Terraform).

Once installed, the extension automatically works with Defender for Cloud, and any detected secret exposure or security issue is surfaced in Defender for Cloud’s “DevOps Security” dashboard.

Configure workflow automation: Applies to automated incident responses, not DevOps integration.

Enable a plan: Refers to enabling Defender plans for specific Azure resource types, not connecting DevOps.

Configure OAuth / Configure security policies: Required for custom integration scenarios, but not for standard Defender–DevOps onboarding.

✅ Final Correct Answer:

In Defender for Cloud: Add an environment

In AzDO1: Install an extension

Enabling User and Entity Behavior Analytics (UEBA) in Microsoft Sentinel requires permissions in both Azure Active Directory (Microsoft Entra ID) and Microsoft Sentinel because UEBA integrates identity data from Azure AD to perform behavioral analytics and anomaly detection.

Here’s the reasoning based on Microsoft’s official documentation and the principle of least privilege:

1️⃣ Azure AD role → Security administrator

The Security Administrator role in Azure AD allows a user to manage security-related features, including security settings and integration of identity signals with other services like Microsoft Sentinel.

This role has the rights necessary to grant Sentinel access to Azure AD data for UEBA without requiring broader, high-privilege roles such as Global Administrator.

Microsoft documentation explicitly recommends the Security Administrator role to enable UEBA because Sentinel uses Azure AD identity information and risk detections for its entity behavior modeling.

2️⃣ Azure role → Microsoft Sentinel Contributor

Within Sentinel, the Microsoft Sentinel Contributor role allows a user to configure settings, enable features like UEBA, and manage analytic rules, workbooks, and connectors, but does not grant rights to access workspace data directly (which would be excessive).

It’s the appropriate role for managing Sentinel features while adhering to the least privilege principle.

The Sentinel Responder role is too limited—it can handle incidents but cannot enable or configure UEBA.

✅ Final Answer:

Azure AD role: Security administrator

Azure role: Microsoft Sentinel Contributor

According to Microsoft Defender for Cloud Apps (formerly Microsoft Cloud App Security) documentation, when an application discovered by Cloud Discovery is deemed risky or non-compliant, the recommended remediation process is to unsanction it and enforce blocking through your network security devices or firewalls.

The process follows these four exact steps:

Select the app – In the Discovered apps tab, security analysts must first locate and select the risky application (in this case, Launchpad).Microsoft documentation states: “Select the discovered app that you want to remediate to open available actions.”

Tag the app as Unsanctioned – Marking an app as Unsanctioned flags it as not allowed for organizational use. This designation helps track and automatically block or monitor it through connected security controls.Reference guidance: “Unsanction apps that you want to block across your network to prevent user access.”

Generate a block script – Once an app is tagged as Unsanctioned, you can generate a block script compatible with your firewall or proxy device (such as Palo Alto, Cisco ASA, or Zscaler).Microsoft documentation explains: “From the unsanctioned app menu, select Generate block script to create a script for your appliance type.”

Run the script on the source appliance – Finally, to enforce blocking, the generated script must be executed on the network appliance or proxy device that is integrated with Cloud Discovery.Official description: “Run the generated script on your network appliance to block unsanctioned apps.”

This sequence ensures compliance and reduces data exposure by automatically restricting high-risk apps, aligning with Microsoft SecOps best practices of proactive risk mitigation and least privilege.

✅ Final Correct Order:

1. Select the app → 2. Tag the app as Unsanctioned → 3. Generate a block script → 4. Run the script on the source appliance

Microsoft’s documentation states clearly that the Deep analysis feature in Defender for Endpoint supports only PE files (Portable Executable files) — specifically .exe and .dll binaries. In the “Take response actions on a file” article, Microsoft says:

“Only PE files are supported, including .exe and .dll files.” Microsoft Learn

That means only File2.exe and File3.dll among your three examples are eligible for deep analysis.

Further, a file’s “Deep analysis” is only enabled when the file exists in the Defender backend sample collection or is observed on a supported Windows device. The tool won’t support script files like .ps1 (PowerShell scripts) via that deep analysis mechanism.

To break it down:

File1.ps1 (PowerShell script) is not a PE file, so it cannot be submitted to the deep analysis sandbox that expects an executable or library format.

File2.exe is a standard executable (PE format) and is valid for deep analysis.

File3.dll is a dynamic-link library (also PE format) and is likewise valid for deep analysis.

Thus, the only files you can submit for deep analysis in Microsoft Defender XDR are those in the proper PE format — File2.exe and File3.dll.

In Microsoft Sentinel, when you identify an alert as a false positive for a specific account, the most efficient way to suppress future alerts for that entity—without affecting others—is to use an automation rule.

Microsoft Sentinel documentation explains:

“Automation rules can automatically close incidents or suppress alerts based on conditions such as user, IP, or entity name. This helps reduce noise and prevent false positives without modifying analytics rules or affecting other entities.”

By configuring an automation rule to auto-close or suppress alerts where the username equals the known account, Sentinel will prevent new alerts for that account while continuing normal detection for others.

Other options are incorrect:

Watchlist (B) only stores reference data; it doesn’t suppress alerts.

Modify analytics rule (C) affects all users, not just one account.

Activity templates (D) are used for entity behavior analysis, not alert suppression.

✅ Correct answer: A. Create an automation rule

On the Microsoft Defender (Microsoft 365 Defender) Incident page, investigators need a complete view of what actions were taken and when. The UI provides multiple panes to support that: the Tasks area (lists manual and automated investigation/remediation tasks assigned to the incident), the Activity log (chronological audit of user and system actions taken on the incident such as assignments, status changes, playbook runs and remediation actions), and the Alert timeline (a timeline view showing the alerts that make up the incident and the sequence of alerts and related detections/events). Microsoft’s investigation guidance describes all three surfaces as part of the incident investigation workflow: tasks capture work items and owner actions, the activity log provides an auditable history of actions and changes, and the alert timeline visualizes the alert and event sequence that drove the incident. Because the question asks specifically for reviewing the incident tasks that were performed, the incident page exposes the tasks list and also the activity log and alert timeline so you can see when tasks ran, who ran them, what automated playbooks or remediation executed, and how those tasks related to the underlying alerts. For full incident forensics and auditability you use Tasks + Activity log + Alert timeline.

Explanation (concise): The Regulatory compliance dashboard/report shows standards-based compliance posture and related recommendations across resources, not the alert-specific remediation steps. To view how to fix a specific Security Center (Defender for Cloud) alert, you open the alert, choose Take action, and review the Mitigate the threat guidance. Therefore, downloading the Regulatory compliance report does not meet the goal.