AI-102 Sample Questions Answers

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.

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 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

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.

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.

 The code will perform face recognition → No

 The code will list tags and their associated confidence → Yes

 The code will read a file from the local file system → Yes

The given method uses the Computer Vision client library:

It specifies the features:

List < VisualFeatureTypes > features = new List < VisualFeatureTypes > ()

{

VisualFeatureTypes.Description,

VisualFeatureTypes.Tags,

};

That means it will return descriptions (captions) and tags from the image.

The image is opened from the local file system:

using (Stream imageStream = File.OpenRead(localImage))

The analysis call:

ImageAnalysis results = await client.AnalyzeImageInStreamAsync(imageStream, features);

It prints captions and tags with their confidence values.

Statement Analysis

“The code will perform face recognition.”

No.

The requested features are Description and Tags , not Faces .

Face recognition would require VisualFeatureTypes.Faces .

“The code will list tags and their associated confidence.”

Yes.

The foreach (var tag in results.Tags) loop outputs tag name and confidence.

“The code will read a file from the local file system.”

Yes.

It uses File.OpenRead(localImage) which reads from disk.

Final Answer

The code will perform face recognition → No

The code will list tags and their associated confidence → Yes

The code will read a file from the local file system → Yes

Microsoft References

Analyze an image with the Computer Vision API

VisualFeatureTypes enum reference

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

Key Knowledge:

To analyze images with Azure OpenAI, you must use a GPT-4 model with vision support (model version vision-preview ).

This requires deploying a GPT-4 model for vision.

Then you use the Chat playground (not completions, not DALL-E) since you want analysis, not generation.

You need to give the assistant instructions in the system message field (e.g., “You are an AI assistant that describes images”).

Finally, you upload an image and prompt the model to describe it.

Correct Sequence:

Create a new deployment, select a GPT-4 model, and set Model version to vision-preview.

Because GPT-4 with vision is required.

Open Chat playground and select the deployed model.

Vision capabilities are accessed through the Chat playground.

In the System message field, enter: “You are an AI assistant that describes images.”

This sets the assistant’s role.

In the Chat session pane, enter a text prompt of “Describe this image,” and upload an image by using the attachment button.

This triggers the model to analyze and describe the image.

Verified Answer (Correct Order):

Create a new deployment, select a GPT-4 model, and set Model version to vision-preview.

Open Chat playground and select the deployed model.

In the System message field, enter: You are an AI assistant that describes images.

In the Chat session pane, enter: Describe this image , and upload an image by using the attachment button.

Microsoft References:

Azure OpenAI vision capabilities

Azure OpenAI Studio overview

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.

Comprehensive Detailed Explanation

In the provided LUDown file snippet:

Intents represent the purpose or action the user wants to perform.

Examples: ## Confirm , ## ExtractName , ## Logout , ## SelectItem , ## SelectNone .

These are all intent definitions.

So, SelectItem is an intent.

Utterances are the example phrases that users might say to invoke a particular intent.

Under ## SelectItem , one example utterance is:

choose { @DirectionalReference =top right}

This is an example phrase that maps to the SelectItem intent and contains the DirectionalReference entity.

Therefore, Choose {@DirectionalReference=top right} is an utterance.

Why not the other options?

Domain: Predefined category grouping multiple intents (not applicable here).

Entity: Extracted data within utterances (e.g., @DirectionalReference=top right or @personName ).

The entire phrase " choose {@DirectionalReference=top right} " is not an entity but an utterance containing an entity.

Correct Answers

SelectItem → an intent

Choose {@DirectionalReference=top right} → an utterance

Microsoft References

LUDown file format for LUIS

LUIS concepts: intents, utterances, and entities

1. Add a new application

2. Add a prebuilt domain intent ToDo (it has already utterances so we can skip this step)

3. Train

4. Publish

Azure Synapse Analytics (formerly Azure SQL Data Warehouse) uses a massively parallel processing (MPP) architecture for big data and analytics workloads.

Control Node:

Acts as the brain of the system.

Receives queries, optimizes them, and then coordinates execution.

Does not perform large-scale data crunching itself.

Compute Nodes:

Perform the actual parallel execution of queries.

Data is distributed across these nodes.

The MPP engine breaks down queries into smaller tasks and distributes them to compute nodes for fast, parallel execution.

Review of Answer Options:

Distributes processing across compute nodes → Correct. This is the essence of MPP.

Distributes processing across control nodes → Incorrect. The control node does not process queries in parallel; it coordinates them.

Redirects client connections across compute nodes → Incorrect. Client connections go to the control node, not directly to compute nodes.

Redirects client connections across control nodes → Incorrect. There is typically one control node per Synapse instance.

Correct Answer: Distributes processing across compute nodes

Microsoft References

Azure Synapse Analytics architecture

Distributed query processing in Azure Synapse

You are analyzing an image with Azure AI Content Safety using the following request payload:

{

" image " : {

" content " : " < base_64_string > "

},

" categories " : [

" Violence "

],

" outputType " : " EightSeverityLevels "

}

Key Points:

Category specified → " Violence "

The Content Safety API will check for violent content in the image.

Test image description → A simple circle.

This is not violent in any way.

Output type → " EightSeverityLevels "

This setting returns a floating-point severity score from 0.0 (no severity) up to 7.0 (highest severity).

Expected result:

Since the image is only a circle (non-violent), the violence severity level will be at the lowest possible value: 0.0

Correct Answer: B. 0.0

Microsoft References

Azure AI Content Safety – Image moderation

Content Safety severity levels

The agent will reason over the uploaded file.●○

The code will create a run and check the output.○●

The code will create an agent and enable a file search.○●

 The agent will reason over the uploaded file.

Yes : The code includes file = project_client.agents.upload_file_and_poll(file_path= " ./data/file1.zip " , purpose=FilePurpose.AGENTS). The upload_file_and_poll method with FilePurpose.AGENTS suggests that the file (file1.zip) is uploaded for the agent to process, and agents in the Azure AI Foundry Agent Service are typically designed to reason over uploaded data. This indicates the agent will reason over the uploaded file.

 The code will create a run and check the output.

No : The provided code snippet does not include any explicit call to create a run (e.g., a method like create_run or start_run) or check the output (e.g., a method to retrieve or poll run results). The code only uploads a file and creates a vector store, but it does not initiate or monitor a run.

 The code will create an agent and enable a file search.

No : The code does not contain a method to create an agent (e.g., create_agent) or explicitly enable a file search. The create_vector_store_and_poll method creates a vector store, which is typically used for indexing data for search, but there is no indication of enabling a file search or creating an agent in this snippet.

The requirement is to extract structured information (invoice items, sales amounts, customer details) from scanned invoice images.

Form Recognizer (now part of Azure AI Document Intelligence) is specifically designed for extracting structured data from documents such as invoices, receipts, and business forms.

Custom Vision → used for image classification or object detection, not for text/data extraction.

Computer Vision → can extract text with OCR, but it doesn’t provide structured field-level extraction for invoices.

Immersive Reader → for accessibility (reading content aloud, translating text), not document data extraction.

Correct Answer: D

Azure AI Document Intelligence offers pre-built models for common document types like invoices, which extract key fields (e.g., vendor, amount, date) with minimal setup. For custom forms like internal authorization requests, the pre-built general document model handles layout and text extraction, but to minimize development effort, use the pre-built invoice model where applicable and a custom model only if pre-built options fall short. However, the standard recommendation prioritizes pre-built for both to avoid training.

Azure AI Agent Service projects are created and managed in Azure AI Foundry (ai.azure.com). Foundry provides the “project” workspace where you configure agents, connect tools (like speech and search), and use your Azure OpenAI deployments. Language Studio and Speech Studio are for the classic Language and Speech services respectively, and the Azure portal is used to create resources—not the agent project itself. The Microsoft docs explicitly state you start by creating an Azure AI Foundry project to build agents.

References

What is Azure AI Foundry Agent Service? – “To get started… create an Azure AI Foundry project.” Microsoft Learn

Create a project in Azure AI Foundry (projects organize agents, files, evaluations). Microsoft Learn

Quickstart: Create an agent in Azure AI Foundry Agent Service. Microsoft Learn

In the Bot Framework SDK, if you want to collect structured input step by step, you typically use a Waterfall dialog. Each step in the waterfall represents one piece of logic, such as asking for the size, toppings, or type of food item.

Prompts are used inside waterfall steps to actually ask the user for input (e.g., text prompt, choice prompt, date-time prompt).

Adaptive, Action, Input are concepts more relevant to Power Virtual Agents or Adaptive Dialogs (Composer), not the Bot Framework SDK question context.

Since the question specifically mentions Bot Framework SDK, the correct answer is Waterfall dialog + Prompts.

Correct Answer: C, D

“Monitor online activity” in Content Safety Studio shows usage and moderation results and allows viewing category distributions; it’s meant for observability of live traffic , not for running offline tests on sample questions to tune filter settings. Hence, it doesn’t meet the requirement. Content Safety Studio

References

Azure AI Content Safety – Protected material detection concepts & quickstart.

Azure OpenAI – System message (metaprompt) guidance. Microsoft Learn

Content Safety Studio – Monitor online activity page. Content Safety Studio

Azure OpenAI content filtering system and configuration overview.

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 is:

" Measure the public perception of your brand on social media using natural language processing (NLP). "

A. Content Moderator

Designed to filter offensive or inappropriate content in text, images, and videos.

Not suitable for sentiment analysis or measuring perception.

B. Form Recognizer

Extracts structured data from documents (invoices, receipts, forms).

Not used for analyzing social media posts.

C. Computer Vision

Analyzes images and videos for object detection, image classification, OCR, etc.

Not used for text-based sentiment analysis.

D. Language service ✅

Provides natural language processing (NLP) capabilities such as sentiment analysis, opinion mining, key phrase extraction, entity recognition , and language detection .

Perfect fit for analyzing public perception from social media posts .

Upload to: An Azure Storage account

Model type: Custom neural

You have 1,000 scanned hand-written survey responses with no consistent layout. The goal is to extract data while minimizing development effort.

Step 1: Upload location

For Azure AI Document Intelligence (formerly Form Recognizer), training data (images, PDFs) must be placed in a container in an Azure Storage account.

Options like Cosmos DB or Azure Files share are not valid training data sources for Document Intelligence.

Therefore, you upload the survey images to Azure Storage account.

Step 2: Model type

The surveys are handwritten and do not have a consistent layout.

Custom template models work only when the layout is fixed and consistent (e.g., invoices with the same fields in the same place).

Identity document (ID) model is a prebuilt model for passports, driver’s licenses, etc., not for surveys.

Custom neural model is designed to handle unstructured or variable-layout forms, including handwritten content. This best fits the survey scenario.

Thus, the correct configuration is:

Upload to Azure Storage account

Use Custom neural model

Correct Answer:

Upload to: An Azure Storage account

Model type: Custom neural

Microsoft References

Azure AI Document Intelligence – Custom models

Custom neural vs custom template models

Train a custom model with Azure Storage input

B: The Text Analytics API ' s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as " negative " , " neutral " and " positive " ) and confidence scores at the sentence and document-level.

D: 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.

This capability is useful for content stores that collect arbitrary text, where language is unknown. Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- sentiment-analysis?tabs=version-3-1

https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- language-detection

To detect common data types such as temperatures, currency values, email addresses, and telephone numbers with minimal development effort, you should use pre-built entity components in Conversational Language Understanding (CLU). Prebuilt components are ready-made recognizers that automatically extract well-known patterns from utterances—no training, labeling, lists, or regex authoring required.

Specifically, CLU offers the following relevant prebuilt components:

Quantity.Temperature – extracts temperatures (e.g., “34 degrees celsius”).

Quantity.Currency – extracts monetary amounts and currency units (e.g., “$20”, “£15.50”).

Email – extracts email addresses.

Phone Number – extracts phone numbers.

These components are designed to be dropped into your entities and immediately provide predictions, which minimizes development effort compared to:

List entities (you must curate values/synonyms yourself),

Learned entities (require labeling utterances and training),

Regular expression components (you must craft and maintain regex patterns), or

Utterances (training data, not a capability for detection by itself).

Microsoft References

Supported prebuilt entity components in CLU (shows Quantity.Temperature, Quantity.Currency, Email, Phone Number, etc.). Microsoft Learn

Entity components in CLU (explains prebuilt, learned, list, and regex components and why prebuilt components are automatically detected). Microsoft Learn

To let an agent use public web data with recency controls (e.g., limit to content from the last 90 days), you add the Bing grounding tool. In the Azure AI Foundry Agent Service SDK, you create a connection (e.g., search_connection_id ) and then construct a BingGroundingTool with that connection. The tool exposes a list of definitions (tool operations) that the agent can call at runtime.

When creating the agent, you pass tool definitions via the tools= argument (a list of ToolDefinition ). The tool_resources= argument is used when the tool requires additional resource descriptors (e.g., Azure AI Search index configuration). For Bing grounding, providing the connection and passing the definitions as tools= is sufficient. This enables the agent to call Bing with date filters to retrieve only recent, publicly accessible content.

Microsoft Azure AI References (titles only)

Azure AI Agent Service – Bing web grounding tool

Azure AI Agent Service – Create an agent (agent creation parameters including tools and tool_resources )

Azure AI Agent Service – Tool connections and definitions

To detect hateful content with Azure AI Foundry Content Safety – Text , you should read the category analysis results returned by the analyze_text call. The response exposes a collection named categories_analysis, where each item contains the analyzed category (e.g., TextCategory.HATE) and its severity level.

Hence:

Iterate over response.categories_analysis, filter the item where item.category == TextCategory.HATE.

Print the severity using hate_result.severity.

This uses category analysis (not blocklist matches), which is the correct mechanism for detecting hate content with a severity score.

Microsoft References

Azure AI Content Safety – Analyze text (Python quickstart/samples): iterate response.categories_analysis, check TextCategory.HATE, read item.severity.

Azure AI Content Safety – Text categories and severity levels: explains categories (Hate, Violence, Sexual, Self-harm) and severity output.

The statement is:

" When using the Azure Cosmos DB Gremlin API, the container resource type is projected as a ______. "

Options:

graph → Correct. The Gremlin API in Azure Cosmos DB is designed to work with graph data models, supporting vertices (nodes) and edges (relationships). A container is projected as a graph.

table → Used when working with the Table API (key/value store). Not correct here.

partition key → Refers to data distribution and scalability, not the container resource type.

document → Refers to the SQL API or MongoDB API in Cosmos DB, not Gremlin.

Therefore, the correct answer is graph.

Correct Answer: graph

Microsoft References

Azure Cosmos DB Gremlin API

Azure Cosmos DB APIs and data models

In Question Answering (Custom question answering in Azure AI Language), you improve coverage for different user phrasings by adding alternative questions to the same answer. For example, adding “How much does < product > cost?” as an alternate question to the existing Q/A about “What is the price of < product > ?” enables the service to match both user queries to the same answer. After adding alternates, you must retrain and republish the project for changes to take effect. This directly addresses the gap and meets the goal.

References

Add and manage alternative questions in custom question answering; retrain/republish for updates.

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/how-to/contribute-to-project

Custom question answering concepts and authoring workflow.

https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview

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.

For a social media messaging app that needs to determine the language of short text in real time, the most appropriate service is Azure AI Translator. Translator provides a low-latency language detection operation for text, returning the detected language code and a confidence score, and it’s optimized for interactive scenarios like chat and messaging. You can use detection on its own or feed the result directly into translation if needed.

Why not the others:

Azure AI Speech is for audio (speech-to-text, text-to-speech, speech translation), not text-only language detection.

Azure AI Language has language detection within its Text Analytics features, but Translator’s detect endpoint is the recommended, streamlined choice for real-time translation pipelines and chat apps.

Azure AI Foundry Content Safety is for moderation (toxicity, harassment, hate), not language identification.

Microsoft References

Azure AI Translator — Text Translation overview (includes language detection).

https://learn.microsoft.com/azure/ai-services/translator/

Text Translation REST API v3.0 — Detect operation.

https://learn.microsoft.com/azure/ai-services/translator/reference/v3-0-detect

Quickstart: Use the Translator text client libraries (shows detect + translate flow).

https://learn.microsoft.com/azure/ai-services/translator/quickstart-text-sdk

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

Step 1: The Authorization Header

When calling Azure AI Document Intelligence REST API, the correct header to authenticate is:

-H " Ocp-Apim-Subscription-Key: {yourKey} "

Key1 and Secret are not valid header names.

Subscription-Key is also not the standard header.

Correct choice: Ocp-Apim-Subscription-Key

Step 2: Selecting the Prebuilt Model

Since the file contains tabular data (tables, structured document), the right prebuilt model is:

prebuilt-document → extracts text, tables, structure, key-value pairs.

prebuilt-contract → specialized for contracts.

prebuilt-layout → extracts layout, lines, and words but not tables in structured form.

prebuilt-read → simple OCR only.

So the correct choice is: prebuilt-document

Final Completed Command

curl -i -X POST " {endpoint}/formrecognizer/documentModels/prebuilt-document:analyze?api-version=2023-07-31 " \

-H " Content-Type: application/json " \

-H " Ocp-Apim-Subscription-Key: {yourKey} " \

-d " { ' urlSource ' : ' https:// < your-storage > /test.pdf ' } "

Verified Answer:

Left box: Ocp-Apim-Subscription-Key

Right box: prebuilt-document

Microsoft References:

Azure AI Document Intelligence REST API

Prebuilt Document model

The question describes:

An Azure subscription with an Azure AI Content Safety resource.

A social media application where users can upload images.

The requirement is to moderate inappropriate content in those uploaded images.

In Azure AI Content Safety SDKs:

You create a ContentSafetyClient by providing the endpoint and key.

The ContentSafetyClient exposes methods such as:

AnalyzeText() → For text moderation

AnalyzeImage() → For image moderation

Let’s review the code structure:

public static void Analyze ( AnalyzeImageOptions request )

{

var endpoint = Environment.GetEnvironmentVariable( " ENDPOINT " );

var key = Environment.GetEnvironmentVariable( " KEY " );

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

return client.AnalyzeImage(request);

}

First dropdown: must be ContentSafetyClient to instantiate the correct client for Azure AI Content Safety.

Second dropdown: since the requirement is image moderation, we must call client.AnalyzeImage(request) .

Other options:

BlocklistClient → used for custom blocklists, not full moderation.

TextCategoriesAnalysis → refers to text analysis categories, not suitable here.

AnalyzeText → would be used only for text moderation.

Therefore, the correct completion is:

ContentSafetyClient

client.AnalyzeImage(request)

Correct Answer:

First dropdown: ContentSafetyClient

Second dropdown: client.AnalyzeImage(request)

Microsoft References

Azure AI Content Safety – Analyze images

Azure AI Content Safety SDK documentation

ContentSafetyClient class

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

Comprehensive Detailed Explanation

You are asked to construct a URI for the Microsoft Translator API call that:

Translates webpage content into Greek (el).

Returns a transliteration in the Roman alphabet (Latin script).

The base URI given is:

https: //api.cognitive.microsofttranslator.com/translate?api-version=3.0

Now, let’s evaluate the required query parameters.

1. to=el

This specifies the target language for translation.

Since the requirement is to translate into Greek, we must include:

to =el

Correct

2. textType=html

The source content is from a webpage.

Translator API requires textType=html for web page or HTML-formatted content.

If omitted, the default is plain text.

Correct

3. toScript=Latn

By default, Greek text is written in its native Greek script.

To provide a transliteration in the Roman alphabet, we must set toScript=Latn .

Latn = Latin script.

Correct

Incorrect Options

A. toScript=Cyrl

Cyrl = Cyrillic script. Not required here.

B. from=el

This would mean the source language is Greek.

In the requirement, the target is Greek, so this is incorrect.

E. textType=plain

Incorrect, since we are translating HTML webpage content, not plain text.

Correct Answer: C. textType=html, D. to=el, F. toScript=Latn

Microsoft References

Translator Text API v3.0 Reference – Translate

Language and Script Parameters (to, from, toScript)

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

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

Box 2: /translate

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

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.

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.

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.