AI-102 Microsoft Designing and Implementing a Microsoft Azure AI Solution Free Practice Exam Questions (2025 Updated)

Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an “entity for price” would not make the chatbot correctly answer “How much does cost?”. The correct fix is to author additional alternate phrasings (as in Q266).

References

Custom question answering capabilities (no entity modeling).https://learn.microsoft.com/azure/ai-services/language-service/question-answering/overview

CLU entities vs. QnA authoring—separate features and workflows.https://learn.microsoft.com/azure/ai-services/language-service/conversational-language-understanding/overview

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

Platform as a service (PaaS) database offerings in Azure require less setup and configuration effort than Infrastructure as a Service (IaaS) database offerings.

Yes

PaaS abstracts away the OS, patching, backups, and much of the infrastructure management. IaaS requires users to configure and manage VMs and databases manually.

Platform as a service (PaaS) database offerings in Azure provide end users with the ability to control and update the operating system version.

No

In PaaS, the OS and platform management are handled by Azure. Users cannot directly control or update the OS version. This is a responsibility difference between IaaS and PaaS.

All relational and non-relational platform as a service (PaaS) database offerings in Azure can be paused to reduce costs.

No

Not all PaaS database services support pausing. For example, Azure SQL Database serverless can auto-pause, but Azure Cosmos DB and provisioned SQL databases cannot. Hence this statement is incorrect.

Yes

No

No

Correct Responses:

Azure SQL Database – PaaS overview

What is PaaS?

Serverless compute tier in Azure SQL Database (auto-pause)

Microsoft References

In the Microsoft Bot Framework SDK, bots communicate using activities (messages, events, conversation updates, etc.).

To respond to events (like conversation started, member added, or custom event activities), you use an ActivityHandler.

The ActivityHandler class provides methods you can override, such as OnMessageActivityAsync, OnMembersAddedAsync, or custom event handlers, to send custom text responses.

Other options:

Adaptive card (A) → used for rich UI responses, not for handling events.

Dialog (C) → used for managing conversation flow, not event handling.

Skill (D) → used to integrate another bot’s functionality, not for handling custom events.

Correct Answer: B

Analysis:

Azure AI Bot Service (A): Helps build chatbots but does not store grounding data → ❌

Azure SQL (B): Possible storage, but not recommended for vector search grounding → ❌

Azure AI Document Intelligence (C): Extracts structured info, not used directly for grounding → ❌

Azure Blob Storage (D): Required for storing raw files used in retrieval-augmented generation (RAG) → ✅

Azure AI Search (E): Used to index, vectorize, and query data for grounding → ✅

Correct Answer:

D. Azure Blob Storage and E. Azure AI Search

To identify city names with Azure AI Language, you use the Text Analytics client and its Named Entity Recognition (NER) capability. In the .NET SDK, create a TextAnalyticsClient and call RecognizeEntities(...). This returns a CategorizedEntityCollection, where each CategorizedEntity has properties such as Text, Category, SubCategory, and ConfidenceScore.

City mentions are recognized under the broader Location category, and the more specific type is surfaced in SubCategory (commonly "City"). Therefore, iterating the returned entities and filtering with entity.SubCategory.Contains("City") correctly locates city names. Using DocumentAnalysisClient or FormRecognizerClient would be incorrect because those are for document/form extraction scenarios, not general-language NER. QuestionAnsweringClient is for Q&A over knowledge sources. Hence the correct client is TextAnalyticsClient, and the correct discriminator is SubCategory.

Microsoft Azure AI References (titles only)

Azure AI Language – Text Analytics client library: Recognize Entities

Azure AI Language – Named Entity Recognition concepts and categories

Azure AI Language – CategorizedEntity model (Category, SubCategory, ConfidenceScore)

Explanation:

You are building a Text-to-Speech (TTS) solution in Azure Cognitive Services Speech that must:

Convert up to 400 characters (short-form TTS, not long audio synthesis).

Provide multiple voice options to users.

Minimize costs (short-form TTS is cheaper than long-form neural synthesis).

To provide a list of available voices, Azure Speech exposes a REST API endpoint:

Correct endpoint:

https://.tts.speech.microsoft.com/cognitiveservices/voices/list

This endpoint returns a list of all available voices (standard + neural) supported in the region, including locale, gender, and style.

Option A: /longaudiosynthesis/voices

Wrong. This is for long audio synthesis (narration/long documents).

More expensive and not needed for short social media messages.

Option B: /cognitiveservices/voices/list ✅

Correct. This endpoint lists available voices for short-form text-to-speech, which matches the requirements.

Option C: /cognitiveservices/v1?deploymentId={deployment}

Wrong. This is for using a custom voice deployment.

Not required here since the solution should minimize cost and use standard available voices.

Option D: /speechtotext/v3.0/models/base

Wrong. This is for speech-to-text (STT), not text-to-speech.

Evaluating the Options:

Correct Answer:

B. https://uksouth.tts.speech.microsoft.com/cognitiveservices/voices/list

Azure Speech Service REST API reference

List voices in Azure Text-to-Speech

Microsoft References

You are asked to build an app that translates speech from English into French, German, and Spanish using Azure AI Speech Translation.

Step 1 – Define target languages

The SpeechTranslationConfig object requires language codes (not full names like "French", "German", "Spanish").

Valid language codes:

French → "fr"

German → "de"

Spanish → "es"

Therefore, the correct syntax for the output languages is:

var languages = new List { "fr", "de", "es" };

Step 2 – Choose the recognizer type

IntentRecognizer: Used for intent recognition with LUIS, not translation.

SpeakerRecognizer: Used for speaker identification/verification, not translation.

SpeechSynthesizer: Used for text-to-speech, not translation.

TranslationRecognizer: Used specifically for speech-to-speech or speech-to-text translation.

Therefore, the correct recognizer is TranslationRecognizer.

Correct Answer:

Output Languages: {"fr","de","es"}

Recognizer: TranslationRecognizer

Speech Translation with Azure AI Speech

TranslationRecognizer Class (C# API)

Supported language codes

Microsoft References

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.

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

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.

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

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.

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.

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.

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.

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 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.

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.