Microsoft AI-102 Designing and Implementing a Microsoft Azure AI Solution Exam Practice Test

You are securing an Azure Cognitive Search resource with two specific requirements:

Prevent access to Search1 from the internet

The correct way is to use a private endpoint.

A private endpoint connects the Cognitive Search resource directly into your virtual network (VNet), blocking public internet access.

Configuring an IP firewall can restrict access to specific IP ranges but does not fully prevent internet access.

Azure roles and authentication do not address network-level restrictions.

Limit the access of each app to specific queries

This is done via Azure role-based access control (RBAC).

You can assign different Azure roles or API keys with query rights to control what each app can do.

Key authentication alone only provides coarse-grained access (admin/query keys) but does not allow fine-grained per-app restrictions.

RBAC ensures principle of least privilege is enforced.

Correct Answer:

Prevent access from internet → Create a private endpoint

Limit access to queries → Use Azure roles

Microsoft References

Secure access to Azure Cognitive Search using private endpoints

Role-based access control in Azure Cognitive Search

The given data format is:

FirstName,LastName,Age,LeisureHobby,SportsHobby

John,Smith,23,Reading,Basketball

Ben,Smith,21,Guitar,Curling

This format uses commas to separate fields, with the first row as column headers and subsequent rows as records.

This is the definition of CSV (Comma-Separated Values) format.

Now let’s eliminate the other options:

JSON: Uses { } braces and key-value pairs like " FirstName " : " John " . Not shown here.

HTML: Uses < tags > for structure (e.g., < table > , < tr > , < td > ). Not shown here.

YAML: Uses indentation and key-value pairs (e.g., FirstName: John ). Not shown here.

Therefore, the correct format is CSV.

Correct Answer: A. CSV

Microsoft Reference

Common data formats: CSV, JSON, Avro, and Parquet

To make a chatbot use uploaded company data from the Chat playground (“use your data”) , your code must:

Create a chat request (not a plain completion), so you need the ChatCompletionsOptions type for the request options.

This is the options class used for chat completions across Azure OpenAI SDKs and is the one that supports attaching data sources (extensions).

Configure the Azure AI Search data source as a chat extension so the model grounds answers on your indexed documents.

In the SDK, this is done by adding an AzureCognitiveSearchChatExtensionConfiguration entry under the extensions/dataSources collection and setting properties like SearchKey and IndexName . This is the specific configuration object for Azure AI Search integration with Chat Completions.

Therefore, the two dropdowns should be completed as:

First dropdown: ChatCompletionsOptions()

Second dropdown: AzureCognitiveSearchChatExtensionConfiguration

This matches how “Use your data (RAG)” is implemented in Azure OpenAI—chat request + Azure AI Search chat extension—so the bot can answer questions using your uploaded/company data. Microsoft Learn

Microsoft Azure AI Solution References

Azure OpenAI “Use your data” (concepts): how chat uses data sources like Azure AI Search. Microsoft Learn

Azure OpenAI client library for .NET (overview of chat options and extensions support). Microsoft Learn

Chat Completions Options (SDK docs, shows dataSources/extensions concept in chat options).

Examples/discussion of AzureCognitiveSearchChatExtensionConfiguration usage with SearchKey/IndexName.

=========================

Step 1: For the knowledge base, select Show active learning suggestions.

In order to see the suggested questions, on the Edit knowledge base page, select View Options, then select Show active learning suggestions.

Step 2: Approve and reject suggestions.

Each QnA pair suggests the new question alternatives with a check mark, ✔ , to accept the question or an x to reject the suggestions. Select the check mark to add the question.

Step 3: Save and train the knowledge base.

Select Save and Train to save the changes to the knowledge base.

Step 4: Publish the knowledge base.

Select Publish to allow the changes to be available from the GenerateAnswer API.

When 5 or more similar queries are clustered, every 30 minutes, QnA Maker suggests the alternate questions for you to accept or reject.

Cognitive Insights Widget URL

https: / /www.videoindexer.ai/embed/insights/ {accountId}/{videoId}/ ?w idgets=people,keywords & controls=search

Player Widget URL

https: / /www.videoindexer.ai/embed/player/ {accountId}/{videoId}/ ?s howcaptions= true & captions=en- US

Comprehensive Detailed Explanation

The requirements are:

Ensure that users can search for keywords.

In the Cognitive Insights Widget, the controls parameter can be set to search to allow keyword search.

Display the names and faces of people in the video.

The widgets parameter in the Cognitive Insights Widget determines which insights are shown.

To show people and keywords, use people,keywords .

Show captions in the video in English (United States).

For the Player Widget, captions are controlled by two parameters:

showcaptions=true enables captions.

captions=en-US specifies the language.

Final Placement of Values

Cognitive Insights Widget

widgets=people,keywords

controls=search

Player Widget

showcaptions=true

captions=en-US

Microsoft References

Embed Video Indexer widgets

Video Indexer parameters and customization

Comprehensive Detailed Explanation

When you develop a custom question answering project (formerly QnA Maker, now part of Azure Cognitive Service for Language – Question Answering), you can configure it for multi-turn conversations.

Follow-up prompts

Used to create multi-turn conversations.

They allow the system to present additional questions to the user after an initial question, guiding the conversation flow.

Example:

User: " Tell me about Azure Storage. "

Bot: " Which type of storage: Blob, File, Queue, or Table? "

These follow-up options are configured using follow-up prompts.

Correct choice for this question.

Active learning

Helps improve the knowledge base by suggesting alternative questions from user queries.

Not related to multi-turn conversation flow.

Alternate questions

Used to add different phrasings for the same question.

Example: “What is Azure Blob Storage?” and “Explain Blob Storage in Azure.”

This improves recognition but does not enable multi-turn conversations.

Chit-chat

Adds small talk and casual conversation handling (e.g., greetings, jokes).

Not related to multi-turn Q & A about a knowledge base.

Correct Answer: A. Add follow-up prompts

Microsoft References

Multi-turn conversations in Question Answering

Add follow-up prompts in Question Answering

The output will include the following words: our and included. No

The output will include the following words: Paris, Eiffel, and Tower. Yes

The function will output all the key phrases from the input string to the console. No

The output will include the following words: our and included. No

The output will include the following words: Paris, Eiffel, and Tower. Yes

The function will output all the key phrases from the input string to the console. No

The function uses RecognizeEntities to identify entities in the text, which typically includes named entities like locations (e.g., Paris, Eiffel Tower). It then prints the text of each recognized entity. The input string " Our tour of Paris included a visit to the Eiffel Tower " contains the entities " Paris " and " Eiffel Tower, " which will be broken down into individual words like " Paris, " " Eiffel, " and " Tower. " However, common words like " our " and " included " are not typically recognized as entities, and the function does not output all key phrases, only the recognized entities.

The Text Moderation API of Azure Content Moderator is specifically used for scanning text to detect potentially inappropriate or undesired content.

Key features include:

Adult classification score → Indicates the likelihood that the text contains adult content.

Racy classification score → Indicates the likelihood that the text contains sexually suggestive but not explicit content.

PII detection (personal data) is part of text moderation but is not always required when focusing strictly on enabling content moderation for chatbot text safety.

OCR is for extracting text from images, not for moderating chatbot conversations.

Text classification in this context refers to general machine learning categorization (not part of Content Moderator’s moderation API).

Thus, the two service responses directly tied to text moderation safety are:

Adult classification score

Racy classification score

Correct Answer for Q185: A and E

Microsoft Reference

Azure Content Moderator text moderation overview

account

The statement is:

" When provisioning an Azure Cosmos DB ______, you need to specify which type of API you will use. "

Options:

account → Correct. When you create a Cosmos DB account, you must choose the API type (e.g., SQL API, MongoDB API, Cassandra API, Table API, Gremlin API). This decision defines the data model and query language. ✅

container → A container is created inside a database (tables, collections, graphs) but does not determine the API type.

database → Exists inside an account and inherits the API type from the account.

item → Represents individual records/documents; not where the API is chosen.

Thus, the API choice is made at the account level.

Correct Answer: account

Microsoft References

Azure Cosmos DB account overview

Choose the right API for Cosmos DB

1. Number of Projection Groups

A projection group in a knowledge store is an array of projections ( tables , objects , or files ) that are stored together.

In the provided JSON, the projections array contains the following two top-level sections:

The array of tables :

JSON

" tables " : [

{ ... },

{ ... }

]

The array of objects :

JSON

" objects " : [

{ ... }

]

Each of these top-level arrays ( tables and objects ) represents a distinct projection group in the knowledge store. Therefore, there are two projection groups .

2. Image Saving

The files projection is used to save images, normalized images, or application files generated during the enrichment process.

In the provided JSON, the files array is present but is empty :

JSON

" files " : []

Since there are no file projections defined, the images (or any files) generated during the enrichment process will not be saved to the knowledge store. They are processed in the pipeline but not persisted in the output storage defined by the knowledgeStore configuration.

You have a chatbot built on Azure OpenAI (AI1). The requirement is that all input and output from the chatbot must be checked for objectionable content (such as hate speech, sexual, violent, or self-harm content).

To meet this requirement, Microsoft recommends integrating Azure AI Content Safety with Azure OpenAI.

Azure AI Content Safety provides APIs to detect and filter harmful or objectionable text and images.

For chatbot scenarios, you can configure Content Safety as a moderation layer on both user input (prompt) and model output (response).

This ensures safe interactions before messages are returned to users.

Other options:

A. Azure Machine Learning – used for building, training, and deploying custom ML models, not for content moderation.

B. Log Analytics – used for monitoring and logging, not for filtering objectionable content.

D. Microsoft Defender Threat Intelligence (Defender TI) – provides cybersecurity intelligence, not responsible for moderating chatbot content.

Therefore, the correct resource to create is Azure AI Content Safety.

Correct Answer: C. Azure AI Content Safety

Microsoft References

Azure OpenAI and Azure AI Content Safety integration

Azure AI Content Safety overview

Moderate text with Azure AI Content Safety

Customer-managed key (CMK) encryption does not affect throttling.

Instead, you could migrate to a Cognitive Search service that uses a higher tier.

Note: A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.

To let an Azure AI Agent access publicly available, recent web content, you add the Bing grounding tool. In the SDK, you first define a ToolConnectionList with the Bing connection ID (here, " bingConnectionId " ), then construct a BingGroundingToolDefinition(connectionList) so the agent can call Bing Search with recency filters (e.g., last 90 days) when answering questions.

When creating the agent, tool definitions are passed via the tools: parameter of CreateAgentAsync(...) as a List < ToolDefinition > . The toolResources parameter is used when you must also supply resource descriptors (for example, an Azure AI Search index configuration), which is not needed here because the Bing grounding tool already references the connection through the ToolConnectionList .

Therefore:

Use BingGroundingToolDefinition to ground the agent on current public web data.

Provide that tool in tools: new List < ToolDefinition > { grounding } when calling CreateAgentAsync(...) .

Microsoft Azure AI References (titles only)

Azure AI Agent Service – Bing web grounding tool (tool definitions and connections)

Azure AI Agent Service SDK – Agent creation ( CreateAgentAsync parameters: tools vs toolResources )

Azure AI Agent Service – Tool connections and ToolConnectionList usage

Comprehensive Detailed Explanation

You have an Azure Cognitive Search service. Query volume has increased, and some search requests are now being throttled. This means your current tier is no longer sufficient to handle the query traffic.

Why does migrating to a higher tier work?

Each Azure Cognitive Search pricing tier (Basic, Standard S1/S2/S3, Storage Optimized, etc.) provides different resource limits for queries per second (QPS), indexing throughput, and storage.

Throttling occurs when query traffic exceeds the capacity limits of the current tier.

Moving to a higher tier increases:

The allowed query units (QUs),

Maximum queries per second (QPS),

and available compute resources.

Therefore, migrating to a higher tier reduces the likelihood of throttling and supports the increased query volume.

Why not other solutions?

Simply adding replicas can help scale out query workloads, but the question specifically asks whether moving to a higher tier meets the goal—and it does.

Using indexer scaling or adjusting query patterns might help, but they are not direct answers to the throttling caused by insufficient service tier capacity.

Correct Answer: A. Yes

Microsoft References

Azure Cognitive Search service limits by tier

Scale resources in Azure Cognitive Search

Azure Cognitive Search pricing tiers

According to the Microsoft documentation, Computer Vision is a cloud-based service that provides developers with access to advanced algorithms for processing images and returning information. By uploading an image or specifying an image URL, Computer Vision algorithms can analyze visual content in different ways based on inputs and user choices.

According to the Microsoft documentation, image type detection is one of the features of Computer Vision that can categorize an image as either a photograph or a drawing. You can use the image type detection feature by calling the Analyze Image API with the visualFeatures parameter set to ImageType. The API will return a JSON response with an imageType field that indicates whether the image is a photo or a clipart.

According to the Microsoft documentation, image descriptions is another feature of Computer Vision that can generate a caption for an image. You can use the image descriptions feature by calling the Analyze Image API with the visualFeatures parameter set to Description. The API will return a JSON response with a description field that contains a list of captions for the image, each with a confidence score.

Therefore, by using these two features of Computer Vision, you can achieve your app requirements with minimal development effort. You don’t need to use any other services, such as object detection, content tags, or Custom Vision, which are designed for different purposes.

This question aligns with Microsoft Responsible AI principles (Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, and Accountability).

The scenario:

Sentiment Analysis is used to determine staff bonuses (a financial impact).

Automated sentiment classification can be biased or produce incorrect results.

Decisions that affect people’s financial or legal status should never be made fully automated.

Analysis of the options:

A. Add a human review and approval step → Correct. This ensures accountability and fairness, reducing harm from potential model bias or errors.

B. Include results even with low confidence → Wrong. That increases risk of incorrect decisions.

C. Use all surveys including deleted accounts → Wrong. Violates privacy and compliance.

D. Publish raw survey data → Wrong. Violates privacy and security principles.

Therefore, the best responsible AI-compliant action is to add human review before making financial decisions.

Correct Answer for Q186: A

Microsoft Reference

Microsoft Responsible AI principles

Responsible use of AI in decision-making

Comprehensive Detailed Explanation

The scenario:

You are using Azure Content Moderator to filter chatbot messages.

You must detect sexually explicit language.

The question asks: which section of the response provides the category score, and which category number corresponds to sexual content.

Step 1 – Understand Content Moderator response structure

Content Moderator returns several sections:

Classification → Provides scores for categories of potentially unsafe or inappropriate content.

PII → Identifies personally identifiable information (phone numbers, email, addresses, etc.).

Terms → Identifies terms from a custom or built-in blocklist.

Since the requirement is sexually explicit language detection, the correct section is Classification.

Step 2 – Identify the correct Category

Content Moderator’s Classification section returns three default categories:

Category 1: Potentially sexually explicit or adult content.

Category 2: Potentially sexually suggestive content.

Category 3: Offensive or derogatory language.

Since the question specifies sexually explicit language, the correct category is 1.

Correct Answer Mapping:

Section: Classification

Category: 1

Microsoft References

Content Moderator – Text moderation (classification categories explained)

Content Moderator overview and response schema

In user want to change to deploy offline model

1. Change model domain to compact model

2. Retrain compact model

3. Export model

Semi-structured data

Let’s break down the options:

Graph data

Used in graph databases (e.g., Azure Cosmos DB Gremlin API, Neo4j).

Represents nodes and edges with relationships.

JSON is not graph data.

Relational data

Stored in relational databases with tables, rows, and columns.

JSON does not follow a fixed schema with rows/columns.

Semi-structured data

Correct.

JSON, XML, and Avro are common examples of semi-structured data.

They don’t require a rigid schema like relational data, but still have organizational elements (tags, key-value pairs).

JSON is widely used in NoSQL/document databases like Azure Cosmos DB.

Unstructured data

Examples: images, videos, free text, audio.

JSON does not fit here because it has a defined structure with keys and values.

Correct Answer: Semi-structured data

Microsoft References

Structured, semi-structured, and unstructured data

What is semi-structured data?

Azure Cosmos DB and JSON data model

Searchable → Field content is tokenized and added to the full-text index (supports free-text queries).

Retrievable → Field values can be included in search results.

Sortable → Used for ordering results, not required here.

Facetable → Used for aggregation/filtering by categories, not required here.

Filterable → Used for structured filters (e.g., WHERE conditions), not required here.

Comprehensive Detailed Explanation

You are tasked with building a solution using Custom Vision to detect faults in factory components. Custom Vision follows a defined workflow:

Create a project

Before anything else, you must create a Custom Vision project in the Azure AI portal.

This project specifies the type of model you are building: classification (classifying entire images) or object detection (locating items within images).

Since the question is about detecting faults in specific components (a business-specific scenario), it aligns with classification unless explicitly stated otherwise.

Upload and tag images

You must provide training data.

Upload a set of images showing both faulty and non-faulty components.

Tag each image correctly so that the model can learn from labeled examples.

Train the classifier model

Once images are tagged, you run training on the dataset to generate the classifier model.

The model can then be published and used for inference via API calls.

Why not the other options?

Initialize the training dataset: This is not a direct step in Custom Vision; instead, uploading and tagging images inherently prepares the training dataset.

Train the object detection model: Object detection is used if you need bounding boxes around items within an image. Since the question is about recognizing faults in a component (faulty vs non-faulty), a classifier model is appropriate.

Correct Sequence:

Create a project

Upload and tag images

Train the classifier model

Microsoft References

Quickstart: Build an image classification project with the Custom Vision portal

Custom Vision overview

Train and test a Custom Vision model

" Customer-managed keys require an additional billable service, Azure Key Vault, which can be in a different region, but under the same subscription, as Azure Cognitive Search. Enabling CMK encryption will increase index size and degrade query performance. "

same document also lists Azure Key Vault as a requirement:

https://docs.microsoft.com/en-us/azure/search/search-security-overview#data-protection

To detect harmful content with Azure AI Content Safety using the .NET SDK, you instantiate a ContentSafetyClient with the endpoint and key. For text moderation, you construct an AnalyzeTextOptions object with the input string (and optionally specify categories, blocklists, etc.). You then call client.AnalyzeText(AnalyzeTextOptions options) and inspect the result (e.g., categories analysis and blocklist matches).

Code completed:

var client = new ContentSafetyClient(new Uri(endpoint), new AzureKeyCredential(key));

var request = new AnalyzeTextOptions( " what is the weather forecast for Seattle " );

Response < AnalyzeTextResult > response = client.AnalyzeText(request);

Microsoft Azure AI References

Azure AI Content Safety – SDK usage for text analysis with ContentSafetyClient and AnalyzeTextOptions (Analyze Text API).

Azure AI Content Safety – Concepts: text categories (hate/sexual/violence/self-harm) and blocklists, returned in AnalyzeTextResult .

Detect the incoming language:

Service: Speech to Text

Reasoning: The Speech to Text service can transcribe spoken language into text and includes language identification capabilities, allowing the system to detect whether the caller is speaking French or English.

Respond in the callers ' own language:

Service: Text to Speech

Reasoning: The Text to Speech service can convert text responses into spoken language in the detected language (French or English), enabling the system to respond to callers in their own language.

Ordered Mapping:

Detect the incoming language: Speech to Text

Respond in the callers ' own language: Text to Speech

Box 1: Text Analytics

The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.

Box 2: Translator

Translator is a cloud-based neural machine translation service that is part of the Azure Cognitive Services family of REST APIs. Translator can be used with any operating system and powers many Microsoft products and services used by thousands of businesses worldwide to perform language translation and other language-related operations.

When deploying an Azure OpenAI model using the Microsoft.CognitiveServices/accounts/deployments resource in an ARM template, the throughput for a Standard deployment is set on the SKU object. Specifically:

Use the capacity field of the SKU to specify the per-deployment rate.

Provide the integer value that represents the requests per minute (RPM) you want the deployment to handle.

Therefore, to meet the requirement of 600 requests per minute , complete the template as:

" sku " : {

" name " : " Standard " ,

" capacity " : 600

}

This uses a single deployment SKU with a defined capacity. The ARM schema for accounts/deployments explicitly includes sku.capacity for resources that support scale (Azure OpenAI deployments do), and Azure OpenAI quota/rate-limit guidance confirms that rate limits are assigned per deployment.

Microsoft Azure AI Solution References

ARM template—Microsoft.CognitiveServices/accounts/deployments (2023-05-01) : property reference showing sku.capacity. Microsoft Learn

Resource type reference—latest accounts/deployments : confirms SKU usage on deployments. Microsoft Learn

Azure OpenAI quota and rate limits : explains assigning deployment-level limits (RPM/TPM). Microsoft Learn

To validate that an input language is supported for translation, call the Translator SDK’s GetLanguagesAsync with scope: " translation " to retrieve the supported languages. The call returns a GetLanguagesResult, whose Translation property is a dictionary keyed by language codes (e.g., " en " , " it " ). You can then check membership with ContainsKey(language) . If the key exists, the language is supported; otherwise, it isn’t.

This matches the .NET Translator client design:

TextTranslationClient.GetLanguagesAsync(...) fetches supported languages.

GetLanguagesResult.Translation is a dictionary of translation-capable languages, so ContainsKey is the correct check.

Microsoft References

TextTranslationClient.GetLanguagesAsync (overview and signature).

Languages REST reference — use scope=translation to get languages supported for translation. Microsoft Learn

GetLanguagesResult class — Translation dictionary property.

You have files with the following formats:

File1: WMV (34 mins, 400 MB)

File2: AVI (90 mins, 1,200 MB)

File3: MOV (300 mins, 980 MB)

File4: MP4 (80 mins, 1,800 MB)

You must determine which files can be uploaded to Azure AI Video Indexer.

Key Rules (Video Indexer upload limitations):

Supported formats: MP4, MOV, WMV, AVI, M2TS.

Maximum file length: 4 hours (240 minutes).

Maximum file size: 2 GB (2048 MB) for website uploads.

Check each file:

File1 (WMV, 34 min, 400 MB): Supported format, within time and size → ✅ Allowed.

File2 (AVI, 90 min, 1,200 MB): Supported format, within time and size → ✅ Allowed.

File3 (MOV, 300 min, 980 MB): Supported format, but exceeds 240 min limit → ❌ Not Allowed.

File4 (MP4, 80 min, 1,800 MB): Supported format, within size (under 2 GB), within time → ✅ Allowed.

Correct Answer:

A. File1, File2, and File4 only

Microsoft Reference:

Video Indexer upload limitations

Box 1: mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment

To run the Sentiment Analysis v3 container, execute the following docker run command.

docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \

mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \

Eula=accept \

Billing={ENDPOINT_URI} \

ApiKey={API_KEY} is the endpoint for accessing the Text Analytics API. https:// < your-custom-subdomain > .cognitiveservices.azure.com

Box 2: https://contoso.cognitiveservices.azure.com

{ENDPOINT_URI} is the endpoint for accessing the Text Analytics API: https:// < your-custom-subdomain > .cognitiveservices.a The endpoint for accessing the Text Analytics API. zure.com

To perform sentiment analysis, we specify TextAnalytics, not ContentModerator.

Possible SKU names include: ' F0 ' , ' F1 ' , ' S0 ' , ' S1 ' , ' S2 ' , ' S3 ' , ' S4 ' , ' S5 ' , ' S6 ' , ' S7 ' , ' S8 '

Possible location names include: westus, eastus

You need to restrict access to an Azure AI services (Cognitive Services) account so that only specific resources on a virtual network can reach it and block public (external) access with minimum effort.

The simplest approach is to use Virtual Network service endpoints for Microsoft.CognitiveServices:

Enable the service endpoint on the VNet/subnet (VNet1) for Microsoft.CognitiveServices

This routes traffic from that subnet to the Cognitive Services account over the Azure backbone and allows the account to identify traffic coming from that subnet.

Modify the Cognitive Services account’s network settings (CSAccount1)

Set Public network access: Disabled (or allow access from Selected networks) and add the VNet/subnet enabled with the service endpoint to the allowed list.

This blocks all external access while permitting access from only the specified VNet resources.

Other options are not appropriate:

A. Modify VNet settings (generic) doesn’t target the required service endpoint action.

C. IAM controls user/role authorization, not network access.

D. Create a virtual subnet isn’t necessary if the VNet/subnet already exists; using service endpoints avoids creating/maintaining private endpoints.

Therefore, the correct pair is B and E.

Microsoft Azure AI Solution References

Secure Cognitive Services with virtual network service endpoints – explains enabling Microsoft.CognitiveServices service endpoints on a subnet and restricting the account to selected networks.

https://learn.microsoft.com/azure/ai-services/cognitive-services-virtual-networks

Network access restrictions for Cognitive Services – details configuring “Public network access” and allowing access from selected VNets/subnets.

https://learn.microsoft.com/azure/ai-services/cognitive-services-network-security

Overview of securing Azure AI services – covers options (service endpoints vs. private endpoints) and minimal configuration steps.

https://learn.microsoft.com/azure/ai-services/security-networking

The requirement:

You have a system monitoring temperature data from a single data stream.

The system must generate an alert in response to atypical values.

The solution must minimize development effort.

Analysis of Options:

Univariate Anomaly Detection

Designed for detecting anomalies in a single time series (e.g., temperature, pressure, humidity, sales).

Since the system monitors temperature only, this is a classic univariate case.

Provides simple integration with minimal effort.

Correct choice.

Azure Stream Analytics

Can process real-time streaming data and detect conditions (with custom queries).

However, it requires more development effort since you would need to manually code logic for anomaly detection.

Not the minimal-effort option.

Metric alerts in Azure Monitor

Used to trigger alerts on predefined thresholds (static rules).

Not intelligent anomaly detection—does not automatically learn patterns from data.

Would require manual threshold setup, which is not efficient for anomaly detection.

Multivariate Anomaly Detection

Used when analyzing multiple related time series (e.g., temperature, pressure, vibration all together).

Since this scenario only has one variable (temperature), multivariate is unnecessary and adds complexity.

Correct Answer: A. Univariate Anomaly Detection

Microsoft References

Azure AI Anomaly Detector – Univariate

Multivariate Anomaly Detector

Difference between univariate and multivariate anomaly detection

Box 1: Cognitive Service User

Ensure that the members of a group named Management-Accountants can approve the FAQs.

Approve=publish.

Cognitive Service User (read/write/publish): API permissions: All access to Cognitive Services resource except for ability to:

1. Add new members to roles.

2. Create new resources.

Box 2: Cognitive Services QnA Maker Editor

Ensure that the members of a group named Consultant-Accountants can create and amend the FAQs.

QnA Maker Editor: API permissions:

1. Create KB API

2. Update KB API

3. Replace KB API

4. Replace Alterations

5. " Train API " [in new service model v5]

Box 3: Cognitive Services QnA Maker Read

Ensure that the members of a group named the Agent-CustomerServices can browse the FAQs.

QnA Maker Read: API Permissions:

1. Download KB API

2. List KBs for user API

3. Get Knowledge base details

4. Download Alterations

Generate Answer

Scenario: All planned projects must support English, French, and Portuguese.

If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use the Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model.

The requirements state:

All AI solution responses must have a confidence score ≥ 70%.

If the response confidence score is < 70%, the response must be improved by human input.

Members of the Consultant-Bookkeeper group must be able to process financial documents, which includes performing manual review when the AI confidence is below threshold.

Looking at the provided JSON:

" fields " : {

" ReceiptType " : {

" type " : " string " ,

" valueString " : " Itemized " ,

" confidence " : 0.672

},

" MerchantName " : {

" type " : " string " ,

" valueString " : " Tailwind " ,

" confidence " : 0.913

}

}

ReceiptType.confidence = 0.672 → this is below 0.7.

MerchantName.confidence = 0.913 → this is above 0.7.

Therefore, the correct condition for triggering manual review is:

documentResults.fields. < field > .confidence < 0.7

Now, evaluating the options:

A. documentResults.docType == " prebuilt:receipt "

Always true for receipts, does not check confidence. Not correct.

B. documentResults.fields.*.confidence < 0.7

This is the correct general expression: trigger manual review whenever any field confidence is below 0.7.

C. documentResults.fields.ReceiptType.confidence > 0.7

This would bypass manual review when ReceiptType has high confidence. The requirement is to trigger review when confidence < 0.7, so this is the opposite.

D. documentResults.fields.MerchantName.confidence < 0.7

This only checks one field (MerchantName). In the JSON, MerchantName has confidence 0.913 ( > 0.7), so this condition would not trigger, but ReceiptType clearly needs review. Too narrow.

Correct Answer: B. documentResults.fields.*.confidence < 0.7

Microsoft References

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

Within a search service, synonym maps are a global resource that associate equivalent terms, expanding the scope of a query without the user having to actually provide the term. For example, assuming " dog " , " canine " , and " puppy " are mapped synonyms, a query on " canine " will match on a document containing " dog " .

Create synonyms: A synonym map is an asset that can be created once and used by many indexes.

C: Analyze Receipt - Get Analyze Receipt Result.

Query the status and retrieve the result of an Analyze Receipt operation.

Request URL: https://{endpoint}/formrecognizer/v2.0-preview/prebuilt/receipt/analyzeResults/{resultId}

E: POST {Endpoint}/vision/v3.2/read/analyze

Use this interface to get the result of a Read operation, employing the state-of-the-art Optical Character Recognition (OCR) algorithms optimized for text-heavy documents.

Scenario: Contoso plans to develop a document processing workflow to extract information automatically from PDFs and images of financial documents

The document processing solution must be able to process standardized financial documents that have the following characteristics:

- Contain fewer than 20 pages.

- Be formatted as PDF or JPEG files.

- Have a distinct standard for each office.

*The document processing solution must be able to extract tables and text from the financial documents.

The document processing solution must be able to extract information from receipt images.

Can search content in different formats, including video

Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.

Keywords extraction: Extracts keywords from speech and visual text.

Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).

Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.

Artifacts: Extracts rich set of " next level of details " artifacts for each of the models.

Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.

Step 1: Create a new project and load sample documents

Create a new project. Projects store your configurations and settings.

Step 2: Label the sample documents

When you create or open a project, the main tag editor window opens.

Step 3: Train a custom model.

Finally, train a custom model.

The wiki contains text in English, French and Portuguese.

Scenario: All planned projects must support English, French, and Portuguese.

The Document Extraction skill extracts content from a file within the enrichment pipeline. This allows you to take advantage of the document extraction step that normally happens before the skillset execution with files that may be generated by other skills.

Note: The Translator Text API will be used to determine the from language. The Language detection skill is not required.

Box 1: " categories " : [ " Locations " , " Persons " , " Organizations " ],

Locations, Persons, Organizations are in the outputs.

Scenario: Contoso plans to develop a searchable knowledgebase of all the intellectual property

Note: The categories parameter is an array of categories that should be extracted. Possible category types: " Person " , " Location " , " Organization " , " Quantity " , " Datetime " , " URL " , " Email " . If no category is provided, all types are returned.

Box 2: { " name " : " entities " }

The include wikis, so should include entities in the outputs.

Note: entities is an array of complex types that contains rich information about the entities extracted from text, with the following fields

name (the actual entity name. This represents a " normalized " form)

wikipediaId

wikipediaLanguage

wikipediaUrl (a link to Wikipedia page for the entity)

etc.

Scenario: When the response confidence score is low, ensure that the chatbot can provide other response options to the customers.

When no good match is found by the ranker, the confidence score of 0.0 or " None " is returned and the default response is " No good match found in the KB " . You can override this default response in the bot or application code calling the endpoint. Alternately, you can also set the override response in Azure and this changes the default for all knowledge bases deployed in a particular QnA Maker service.

Choosing Ranker type: By default, QnA Maker searches through questions and answers. If you want to search through questions only, to generate an answer, use the RankerType=QuestionOnly in the POST body of the GenerateAnswer request.

Box 1: Azure Blob storage

At the start of the pipeline, you have unstructured text or non-text content (such as images, scanned documents, or JPEG files). Data must exist in an Azure data storage service that can be accessed by an indexer.

Box 2: Computer Vision API

Scenario: Provide users with the ability to search insight gained from the images, manuals, and videos associated with the products.

The Computer Vision Read API is Azure ' s latest OCR technology (learn what ' s new) that extracts printed text (in several languages), handwritten text (English only), digits, and currency symbols from images and multi-page PDF documents.

Box 3: Translator API

Scenario: Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.

Box 4: Azure Files

Scenario: Store all raw insight data that was generated, so the data can be processed later.

Comprehensive Detailed Explanation

The requirement is to build an Adaptive Card for the chatbot that supports multilingual product display and meets accessibility requirements.

Product Name Display

The product JSON stores localized names like " name " : { " en " : " ... " , " es " : " ... " , " pt " : " ... " } .

To dynamically render based on the current language, you must use name[language].

This ensures the product name shown in the card adapts automatically to the user’s preferred language.

Stock Level Warning

Business rules require warnings when stock is low or out of stock.

This means you display the stock warning only when stockLevel is not ' OK ' .

Therefore, the correct condition is:

" $when " : " ${stockLevel != ' OK ' } "

Image Alt Text for Accessibility

Accessibility requirements specify that all images must have alt text in English, Spanish, and Portuguese.

In the product JSON, " alttext " is also localized:

" altText " : { " en " : " Bicycle " , " es " : " Bicicleta " , " pt " : " Bicicleta " }

To render the correct localized alt text dynamically, use image.altText[language].

Correct Selections:

First blank: name[language]

Second blank: " $when " : " ${stockLevel != ' OK ' } "

Third blank: image.altText[language]

Microsoft References

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

Box 1: api-nam.cognitive.microsofttranslator.com

https://docs.microsoft.com/en-us/azure/cognitive-services/translator/reference/v3-0-reference

Box 2: /translate

Box 1: QnA Maker Editor

Scenario: Provide all employees with the ability to edit Q & As.

The QnA Maker Editor (read/write) has the following permissions:

Create KB API

Update KB API

Replace KB API

Replace Alterations

" Train API " [in

new service model v5]

Box 2: Contributor

Scenario: Only senior managers must be able to publish updates.

Contributor permission: All except ability to add new members to roles

Scenario: All videos must have transcripts that are associated to the video and included in product descriptions.

Product descriptions, transcripts, and all text must be available in English, Spanish, and Portuguese.

Step 1: Upload the video to blob storage

Given a video or audio file, the file is first dropped into a Blob Storage. T

Step 2: Index the video by using the Video Indexer API.

When a video is indexed, Video Indexer produces the JSON content that contains details of the specified video insights. The insights include: transcripts, OCRs, faces, topics, blocks, etc.

Step 3: Extract the transcript from the Video Indexer API.

Step 4: Translate the transcript by using the Translator API.

Parameter type: stream

Visual features list: VisualFeatureTypes.Description

Result to use: results.Description.Captions[0]

To generate accessible alt text , you should use the caption produced by Azure Computer Vision’s Description feature (it produces a human-readable sentence with a confidence score). Therefore:

The image input should be a stream , because you’re uploading images (not just passing URLs) during product creation and AnalyzeImage…Async supports image streams .

Request the Description feature in VisualFeatureTypes so the service returns results.Description.Captions.

Use results.Description.Captions[0] and return the caption text if its confidence is high enough (e.g., > 0.5) to meet the accessibility requirement that all images must have relevant alt text .

Other features (Tags, Objects, Brands) are useful for enrichment but do not directly return natural-language captions suitable for alt text.

Microsoft Azure AI Solution References

Computer Vision (Image Analysis) – Description/Captions and features: Microsoft Docs, Image Analysis – VisualFeatureTypes.Description returns description.captions.

https://learn.microsoft.com/azure/ai-services/computer-vision/concept-image-analysis

SDK usage (analyze image from a stream): Microsoft Docs, Analyze an image by using the Computer Vision client library .

https://learn.microsoft.com/azure/ai-services/computer-vision/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

Scenario: Support autocompletion and autosuggestion based on all product name variants.

A: Call a suggester-enabled query, in the form of a Suggestion request or Autocomplete request, using an API. API usage is illustrated in the following call to the Autocomplete REST API.

POST /indexes/myxboxgames/docs/autocomplete?search & api-version=2020-06-30

{

" search " : " minecraf " ,

" suggesterName " : " sg "

}

B: In Azure Cognitive Search, typeahead or " search-as-you-type " is enabled through a suggester. A suggester provides a list of fields that undergo additional tokenization, generating prefix sequences to support matches on partial terms. For example, a suggester that includes a City field with a value for " Seattle " will have prefix combinations of " sea " , " seat " , " seatt " , and " seattl " to support typeahead.

F. Use the default standard Lucene analyzer ( " analyzer " : null) or a language analyzer (for example, " analyzer " : " en.Microsoft " ) on the field.