AI-102 Designing and Implementing a Microsoft Azure AI Solution Question and 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.

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.

Explanation:

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.

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.

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

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.

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.

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.

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..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

Azure AI Document Intelligence – Confidence scores

Human-in-the-loop for Document Intelligence

Microsoft References

Requirement: The chatbot must classify user input into categories.

The categories must be dynamic and defined at inference time.

Custom text classification (Language service) requires training beforehand and fixed labels.

OpenAI text classification can dynamically handle categories via prompting at inference.

Therefore, Azure OpenAI text classification is correct.

Microsoft Reference:

Classification with Azure OpenAI

You have a Computer Vision resource named contoso1 in West US region.

You must use this resource to resize an image using smart cropping.

Let’s analyze the key points:

Endpoint

Each Azure AI service resource has a regional endpoint.

Since the resource name is contoso1 and is deployed in West US, the correct endpoint will be:

https://contoso1.cognitiveservices.azure.com

The older projectoxford.ai and generic westus.api.cognitive.microsoft.com are legacy endpoints and are not recommended.

Therefore, the correct choice is https://contoso1.cognitiveservices.azure.com .

API Method

The task is to generate a different size of a product photo with smart cropping.

This is done using the Generate Thumbnail API in Computer Vision.

The path for this is:

/vision/v3.1/generateThumbnail

Other options like detect (for object detection) or areaOfInterest (for finding regions of interest) do not generate thumbnails.

Correct Answer:

Endpoint: https://contoso1.cognitiveservices.azure.com

API: /vision/v3.1/generateThumbnail

Computer Vision API reference - Generate Thumbnail

Computer Vision REST API documentation

Azure AI services endpoints

Microsoft References

A private endpoint is a network interface that uses a private IP address from your virtual network. This network interface connects you privately and securely to a service powered by Azure Private Link. By enabling a private endpoint, you're bringing the service into your virtual network.

The service could be an Azure service such as:

Azure Storage

Azure Cosmos DB

Azure SQL Database

Your own service using a Private Link Service.

Azure AI Video Indexer (VI) imposes limits on both file duration and file size:

Duration: Up to 6 hours for all presets; Basic Audio preset supports up to 12 hours.

Size: When uploading from your device, the limit is 2 GB (uploading by URL allows up to 30 GB).

Formats: MP4, AVI (video) and MP3 (audio codec/file) are supported.

Evaluating each file:

File1 – MP4, 34 mins, 1,500 MB: Within 6-hour duration and under 2 GB → Supported.

File2 – AVI, 500 mins (≈8h20m), 1,700 MB: Exceeds the 6-hour limit → Not supported.

File3 – MP3, 300 mins (5h), 980 MB: Under 6 hours (and also under the 12-hour Basic Audio limit) and under 2 GB → Supported.

File4 – MP4, 350 mins (≈5h50m), 2,800 MB: Duration is under 6 hours but size exceeds 2 GB for device uploads → Not supported (unless uploaded by URL, which the question doesn’t state).

Therefore, the files you can upload and analyze (under standard device upload constraints) are File1 and File3 only.

Microsoft References

Support matrix & limits — file duration (6h for all presets; 12h for Basic Audio), device upload size limit 2 GB; supported formats (MP4, AVI, WAV, etc.) and audio codecs including MP3. Microsoft Learn

Release notes — increase of duration limit from 4h to 6h (and 12h for Basic Audio). Microsoft Learn

Upload guidance — device upload 2 GB vs 30 GB via URL.

Generate alt textTo automatically suggest descriptive captions for images (usable as alt text), use the Computer Vision Describe Image API. It returns natural-language captions summarizing the image content. Endpoint: /vision/v3.2/describe. Setting maxCandidates=1 gives a single best caption.

Detect inappropriate contentTo block adult/racy/gory images with minimal effort, use Computer Vision Analyze Image with the Adult visual feature. This returns flags (isAdult, isRacy, isGory) and confidence scores. The provided option includes Adult,Description which still returns the needed adult/racy/gore signals, satisfying the requirement.

These choices minimize development effort by using built-in Computer Vision capabilities without training a custom model or wiring up multiple services.

Microsoft Learn – Describe images (Computer Vision): “The Describe Image API returns a list of natural language sentences that describe the image.”https://learn.microsoft.com/azure/ai-services/computer-vision/overview-image-analysis#describe-images

Microsoft Learn – Analyze images (Adult/Racy/Gore detection): “Use the Adult feature to detect adult, racy, and gory content.”https://learn.microsoft.com/azure/ai-services/computer-vision/overview-image-analysis#adult-racy-and-gory-content-detection

References

“Protected material detection” in Content Safety is designed to flag model outputs that match copyrighted text/code (e.g., lyrics, articles, recipes, GitHub code). It is not used to tune or test content filter configurations for safety categories like hate/sexual/violence/self-harm. Therefore, it does not meet the requirement to optimize content filters by running tests on sample questions.

Comprehensive Detailed ExplanationYou need to analyze confidential documents with the Language service while ensuring those documents never leave your premises. The supported pattern for Azure AI Services (Cognitive Services) is to run the Language service container on-premises and send the app’s requests to that local container endpoint. The container performs inference locally; only metering/billing calls are sent to Azure.

To achieve this:

Provision an on-premises Kubernetes cluster that has internet connectivity.Azure AI service containers running on-prem require outbound access to Azure for metering and billing unless you’re using the special disconnected model (not implied here). Therefore, the cluster must have internet connectivity so the container can reach the Azure billing endpoint while keeping document content local.

Pull an image from the Microsoft Container Registry (MCR).Official Azure AI service containers (including Language) are published in MCR, not Docker Hub. You must pull the Language (Text Analytics/Language) container image from MCR.

Run the container and specify an API key and the Endpoint URL of the Cognitive Services resource.When you start the container, you configure it with the API key and billing (endpoint) URL of your Azure Cognitive Services (multi-service or Language) resource. This enables the container to authenticate and report usage to Azure, while your data stays on-prem.

Why not the other options?

AKS would place workloads in Azure, which isn’t required when the requirement is to keep documents on-prem.

On-prem cluster isolated from the internet would block the container’s required calls to Azure’s billing endpoint.

Pull from Docker Hub is incorrect because Azure AI service containers are published to MCR.

App ID and Client Secret are not the standard parameters for Azure AI service containers; they require API key + billing endpoint.

Use Azure AI services in containers (billing/endpoint and API key requirements, data remains local):Microsoft Learn – Install and run containers for Azure AI services and Use containers with Azure AI services (metering and billing).https://learn.microsoft.com/azure/ai-services/containers/

Language service container images on Microsoft Container Registry (MCR):Microsoft Learn – Deploy language containers (Text Analytics/Language).https://learn.microsoft.com/azure/ai-services/language-service/how-to/use-containers

Networking requirements (internet connectivity for metering):Microsoft Learn – Configure containers and network access for Azure AI services containers.https://learn.microsoft.com/azure/ai-services/containers/container-requirements-and-limits

Microsoft Azure AI Solution ReferencesThese documents collectively show that: (a) containers are pulled from MCR, (b) they are configured with API key + endpoint for billing, and (c) internet egress is required for metering while payload data remains on-premises.

Data Analyst.

When dealing with data roles in an organization, responsibilities are divided among data analysts, data engineers, and data scientists. Let’s review them:

Data Analyst

Focuses on understanding business requirements and ensuring that the right rules and logic are applied to data so it becomes meaningful for decision-making.

They work closely with stakeholders to define business rules such as data validation rules, aggregation logic, and reporting metrics.

This makes them responsible for identifying which business rules must be applied to data.

Data Engineer

Responsible for designing, building, and maintaining the infrastructure and pipelines that move and process data.

They ensure data availability, reliability, and scalability, but do not usually define business rules.

Data Scientist

Focuses on advanced analytics, predictive modeling, and machine learning.

They use data prepared by engineers and analysts but are not typically the ones who define the business rules that govern how raw data should be interpreted.

Therefore, the correct answer is Data Analyst.

Correct Answer: A data analyst

Data roles and responsibilities in Azure

What is a Data Analyst?

Responsibilities of Data Engineers vs Data Scientists vs Data Analysts

Microsoft References

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 cost?” as an alternate question to the existing Q/A about “What is the price of ?” 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

To generate complete sentences describing an image (for accessibility for visually impaired users), you must call the Describe Image API.

describeImageInStreamAsync analyzes the content of the image and generates human-readable descriptions in natural language sentences.

readInStreamAsync is for OCR (extracting text).

tagImageInStreamAsync is for generating tags (keywords), not full sentences.

analyzeImageByDomainInStreamAsync is for domain-specific models (e.g., celebrities, landmarks).

Correct Answer: B

The Computer Vision API uses text recognition APIs to extract and recognize text information from images. Read uses the latest recognition models, and is optimized for large, text-heavy documents and noisy images.

Comprehensive Detailed ExplanationYou 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.

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.

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.

Why does migrating to a higher tier work?Why not other solutions?

Correct Answer: A. Yes

Azure Cognitive Search service limits by tier

Scale resources in Azure Cognitive Search

Azure Cognitive Search pricing tiers

Microsoft References

Scenario Recap:

Intent: FindContact

Training phrases include cities (London, Seattle, Ukraine).

Requirement: Implement phrase list properly.

Solution: Create a new pattern in the FindContact intent.

Analysis:

Patterns in LUIS/CLU are useful for handling specific templates like “Book a flight from {origin} to {destination}”.

They are not the correct way to capture locations like London/Seattle in this scenario.

Instead, locations should be modeled as entities (prebuilt GeographyV2 or custom Location entity).

Correct Answer: B. No

The requirement is to analyze video content to identify mentions of specific company names. This is essentially an audio transcript analysis task (speech-to-text followed by entity recognition). Azure Video Analyzer for Media integrates with Content model customization to detect such entities.

Step-by-step reasoning:

Sign in to the Azure Video Analyzer for Media website.

Azure Video Analyzer for Media (formerly Video Indexer) is the service used for analyzing videos and extracting insights such as speech, text, brands, and entities.

From Content model customization, select Language.

To recognize specific company names in transcripts, you must customize the Language model.

Customization allows you to add vocabulary (e.g., company names) so that speech recognition and transcription are tuned to correctly identify them.

Add the specific company names to the include list.

Adding them to the include list ensures the system actively looks for and recognizes those company names during transcript analysis.

The exclude list would suppress terms, not detect them, so it is not correct in this scenario.

Why not other options?

Sign in to the Custom Vision website → Custom Vision is for image classification/object detection, not audio/video transcript entity recognition.

From Content model customization, select Brands → The Brands model helps detect logos and existing brand mentions, but for custom company names not in the brand catalog, you must customize the Language model.

Exclude list → Would filter out terms, which is opposite of the requirement.

Correct Answer Order:

Sign in to the Azure Video Analyzer for Media website.

From Content model customization, select Language.

Add the specific company names to the include list.

Customize models in Video Indexer

Azure Video Analyzer for Media overview

Content model customization in Video Indexer

Microsoft References

Comprehensive Detailed ExplanationThe 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

Content Moderator – Text moderation (classification categories explained)

Content Moderator overview and response schema

Microsoft References

For a social media messaging app, you need real-time language identification on short text messages. The Azure AI Translator service provides a dedicated Detect API that returns the language and a confidence score for each input text, designed for low-latency scenarios such as chat and in-app communication. This aligns directly with “identify in real time the language used in messages.” Microsoft Azure+2

Why the other options are not the best fit:

Azure AI Speech (A) targets spoken audio (speech-to-text, translation, diarization). It’s not intended for detecting the language of text messages. Azure AI

Azure AI Content Safety (B) focuses on safety/moderation (harmful or unsafe content), not language identification.

Azure AI Language (D) also offers a Language Detection feature, but it’s typically used within broader text analytics workflows. For real-time messaging and potential downstream translation, Translator’s Detect endpoint is the targeted and streamlined choice.

References (Microsoft Learn)

Translator – Detect method (returns detected language + confidence). Microsoft Learn

Translator – Product overview and real-time translation scenarios. Microsoft Azure

Translator – Language support (Auto Language Detection). Microsoft Learn

Azure AI Language – Language Detection (feature overview/how-to) for context.

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

a tabular form of rows and columns

Relational data is, by definition, data that adheres to the relational model.

In the relational model, data is organized into tables, which consist of rows (records or tuples) and columns (fields or attributes). This is known as the tabular form.

The other options are incorrect:

Unstructured data is the opposite of relational data.

A hierarchical folder structure is used for file organization, not the logical structure of relational data.

Comma-separated value (CSV) files can contain relational data, but the core definition of relational data is the tabular structure, not the file format itself.

Rationale

API Endpoint SelectionThe constraint states that the content of the text must remain within the Americas Azure geography.

"api.cognitive.microsofttranslator.com": This is the global endpoint, which doesn't guarantee data residency in a specific geography.

"api-apc.cognitive.microsofttranslator.com": This endpoint refers to the Asia Pacific geography.

"api-nam.cognitive.microsofttranslator.com": This endpoint refers to the North and South America (NAM) geography.

Therefore, to meet the data residency requirement for the Americas, you must select "api-nam.cognitive.microsofttranslator.com".

2. Path/Query String SelectionThe requirement is to translate the text to a single language. The input text can be in "one of many languages," meaning the source language isn't fixed and should be detected automatically. The language to translate to is implied to be English (en) based on the options available and common translation scenarios.

"/translate?from=en": This is incorrect because it specifies the source language as English, which contradicts the "text can be in one of many languages" requirement (implying automatic source language detection).

"/translate?suggestedFrom=en": This is not the standard way to initiate a translation request and seems to suggest a source language, which is unnecessary if auto-detection is desired/possible.

"/translate?to=en": This is the correct and standard way to request a translation where the source language is automatically detected, and the target language is English (en). This fulfills the translation requirement.

"/detect?to=en" / "/detect?from=en": These endpoints are used for language detection and not for the translation itself. The prompt explicitly asks to develop code to translate the text.

Comprehensive Detailed ExplanationThe question focuses on monitoring a public-facing sales system that processes video and text, ensuring that the system provides equitable results regardless of user location or background.

Two Microsoft Responsible AI principles apply directly here:

Fairness

AI systems should treat all people fairly and avoid bias.

Monitoring ensures that outcomes do not disadvantage people based on location, background, or demographics.

This directly addresses the requirement for equitable results.

Inclusiveness

AI systems should empower everyone and engage people of all backgrounds.

Monitoring for inclusiveness ensures that the system works well across different regions, languages, and cultures, making the system accessible and beneficial for all users.

A. Transparency

Important for explaining how AI systems work, but does not directly address equitable results across demographics.

D. Reliability and safety

Focuses on consistent and safe system operation, not equity across users.

E. Privacy and security

Critical for data protection, but not related to equitable treatment of users.

Why not the other options?

Correct Answer:

B. fairness

C. inclusiveness

Microsoft Responsible AI Principles

Fairness in AI systems

Inclusiveness in AI systems

Microsoft References

To mask (redact) phone numbers and email addresses in text using Azure AI Language, you should call the PII entity recognition operation and then print the redacted text that the service returns. In the SDKs, this corresponds to calling RecognizePiiEntities and reading the RedactedText from the result object. The PII feature automatically detects categories such as PhoneNumber and Email and returns a redacted version of the input (masked) in the redactedText/RedactedText field. This is exactly what the code snippet’s “Masked Text” output is asking for.

Therefore:

Use RecognizePiiEntities to detect and redact PII in the document.

Output RedactedText to print the masked string.

Microsoft References

How-to: Detect and redact PII in text (overview and redaction behavior). Microsoft Learn

Quickstart: Detect Personally Identifiable Information (shows PII detection and redacted output). Microsoft Learn

.NET reference: PiiEntityCollection.RedactedText property (“Gets the text of the input document with all PII redacted”). Microsoft Learn

Python client overview: recognize_pii_entities operation for PII detection. Microsoft Learn

To create a resource that can generate fictional stories from prompts, you need an Azure OpenAI account (--kind OpenAI).

To protect data with a customer-managed key (CMK), you must pass the key information (Key Vault URI, key name, key version) via the --encryption parameter when creating the Cognitive Services account. The JSON shown (with keyName, keyVersion, and keyVaultUri) is exactly the payload expected by --encryption.

A complete example pattern:

az cognitiveservices account create \

-n myresource -g myResourceGroup -l westeurope \

--kind OpenAI --sku S \

--assign-identity \

--encryption '{

"keyName":"keyname",

"keyVersion":"secretVersion",

"keyVaultUri":"https:// .vault.azure.net/"

}'

(Adding --assign-identity allows the service to access the Key Vault key; the hotspot specifically asks for the parameter that takes the JSON block and the resource kind, which are --encryption and OpenAI.)

Microsoft References

Azure CLI: Create Cognitive Services account with customer-managed keys using --encryption.https://learn.microsoft.com/azure/ai-services/cognitive-services-encryption-keys#configure-customer-managed-keys

Azure Cognitive Services (Azure AI services) CLI parameters including --kind OpenAI.https://learn.microsoft.com/azure/ai-services/cognitive-services-management-api-cli#create-a-cognitive-services-account

To automatically delete blobs in Azure Blob Storage, you can configure a lifecycle management policy. This policy allows you to:

Transition blobs between hot, cool, and archive tiers.

Delete blobs automatically after a specified number of days.

Other options:

Change feed → Tracks all changes to blobs, but does not delete them.

Soft delete → Protects blobs from accidental deletion by allowing recovery, not automated deletion.

Archive storage → Storage tier for rarely accessed data, not a deletion mechanism.

Correct Answer: a lifecycle management policy

Comprehensive Detailed Explanation

When an indexer runs in Azure AI Search, it processes each document through a fixed series of stages:

Document crackingThe indexer opens the item (file/row) and extracts text, images, and metadata, creating the root of the enriched document (/document). This is always the first stage.

Field mappingsNext, source fields are mapped directly to target index fields before any transformations. Field mappings occur after document cracking but before skillset execution. Microsoft Learn

Skillset executionBuilt-in or custom skills (for example, language and sentiment analysis in your scenario) run and write outputs into the in-memory enriched document. Microsoft Learn

Output field mappingsBecause skill outputs live only in memory, you must define output field mappings to route selected nodes of the enriched document into index fields. This step comes after skillset execution.

Push to indexFinally, the indexer hands off raw and enriched content to the search index (the physical data structures) so it becomes searchable—i.e., the content is “pushed to the index.” Microsoft Learn

This order—document cracking → field mappings → skillset execution → output field mappings → push to index—ensures raw content is extracted, direct source-to-index mappings are applied, AI enrichment is performed, enriched outputs are projected to fields, and the completed documents are written into the index.

Microsoft Azure AI References

Indexers in Azure AI Search – Stages of indexing (Document cracking; Field mappings; Skillset execution; Output field mappings, with field mappings occurring before transformations). Microsoft Learn

Skillset concepts (Output of skillset is routed to the index via output field mappings after skillset execution). Microsoft Learn

AI enrichment concepts (Field mappings move raw source content to the index; output field mappings move enriched content; indexing ingests raw and enriched content into the physical index). Microsoft Learn

Annotations & enriched document tree (Enriched document is created during document cracking at /document). Microsoft Learn

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 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.

You need to monitor a video stream while ensuring the microphone stays enabled and detect when the user asks a question. The simplest/lowest-effort approach is to use Azure AI Speech-to-text to transcribe the user’s audio in real time, then apply a lightweight check (e.g., interrogatives like “who/what/how” or final “?”) to detect a question. Object detection or the Face service analyze visual content (objects/faces/emotions) and cannot tell whether a spoken utterance is a question. Language detection only identifies which language is being spoken; it does not transcribe or classify questions.

References

Azure AI Speech: real-time speech-to-text (streaming) for transcription and downstream analysis.https://learn.microsoft.com/azure/ai-services/speech-service/speech-to-text

Face service overview (not for speech/question detection).https://learn.microsoft.com/azure/ai-services/computer-vision/overview-identity

Custom Vision object detection overview (visual objects, not spoken questions).https://learn.microsoft.com/azure/ai-services/custom-vision-service/object-detection-overview

Language detection capability description (identifies language only).https://learn.microsoft.com/azure/ai-services/language-service/language-detection/overview

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-visi on/how-to/call-analyze-image?tabs=version-3-2#analyze-an-image-from-a-stream

The code snippet shows a helper function provision_resource that provisions Azure Cognitive Services accounts by specifying:

Name (resource name)

Kind (the type of Cognitive Service, e.g., ComputerVision, FormRecognizer, etc.)

Location (Azure region, such as eastus)

SKU tier (pricing tier, e.g., F0 = free, S0 = standard)

Step 1 – Identify the serviceThe dropdown includes ComputerVision, CustomVision.Prediction, CustomVision.Training, and FormRecognizer.

The question context indicates a need to provision an Azure AI service resource, and the answer area highlights the FormRecognizer option. This aligns with document processing scenarios requiring Azure AI Document Intelligence (formerly Form Recognizer).

Step 2 – Select pricing tier and locationThe second dropdown shows combinations of pricing and regions:

"eastus", "S1"

"useast", "S1"

"S0", "eastus"

"S0", "useast"

Azure AI Form Recognizer (Document Intelligence) supports S0 (Standard) and F0 (Free) SKUs in common regions like East US. "S0", "eastus" is a valid and recommended configuration.

Other options like "S1" are not correct here, as Form Recognizer uses S0/F0, not S1.

Correct Answer:

Service Kind: FormRecognizer

Location and SKU: "S0", "eastus"

Azure AI Document Intelligence (Form Recognizer) documentation

Azure Cognitive Services resource creation parameters

Pricing tiers for Form Recognizer

Microsoft References

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.

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: api-nam.cognitive.microsofttranslator.com

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

Box 2: /translate

Comprehensive Detailed ExplanationThe 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]

Adaptive Cards Templating

Adaptive Card $when property

Multilingual data in Azure Cognitive Search and Adaptive Cards

Microsoft References

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.