Pass the Microsoft Azure AI Engineer Associate AI-102 Questions and answers with ExamsMirror

In SQL, commands are categorized into different language groups depending on their purpose:

Data Manipulation Language (DML):

Deals with manipulating the data stored in existing database objects.

Includes:

SELECT → query data

INSERT → add new records

UPDATE → modify existing records

DELETE → remove records

Therefore, UPDATE is a DML statement.

Data Definition Language (DDL):

Used to define and manage database schema objects (tables, indexes, schemas).

Includes: CREATE, ALTER, DROP, TRUNCATE.

Data Control Language (DCL):

Used to control access and permissions.

Includes: GRANT, REVOKE.

Now let’s map the options:

A. REVOKE → DCL (not DML).

B. UPDATE → DML ✅ (correct).

C. DROP → DDL (removes database objects).

D. CREATE → DDL (creates database objects).

Correct Answer: B. UPDATE

SQL Language Elements – DDL, DML, DCL

UPDATE (Transact-SQL)

Microsoft References

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

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

Follow-up prompts

Used to create multi-turn conversations.

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

Example:

User: "Tell me about Azure Storage."

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

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

Correct choice for this question.

Active learning

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

Not related to multi-turn conversation flow.

Alternate questions

Used to add different phrasings for the same question.

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

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

Chit-chat

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

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

Correct Answer: A. Add follow-up prompts

Multi-turn conversations in Question Answering

Add follow-up prompts in Question Answering

Microsoft References

You might want to manage the translation and localization for the language understanding content for your bot independently.

Translate command in the @microsoft/bf-lu library takes advantage of the Microsoft text translation API to automatically machine translate .lu files to one or more than 60+ languages supported by the Microsoft text translation cognitive service.

What is translated?

An .lu file and optionally translate Comments in the lu file

LU reference link texts

List of .lu files under a specific path.

You want to prevent users from sharing sexually explicit images and minimize development effort.

Azure AI Content Safety Studio provides a no-code, browser-based experience to test and tune image moderation using the Content Safety service. It supports out-of-the-box classification for Sexual, Hate, Violence, and Self-harm categories, lets you adjust severity thresholds, review results, and export settings or sample code with minimal engineering work.

Other options:

A. Visual Studio – an IDE, not a moderation service.

B. Vision Studio in Azure AI Vision – focuses on vision tasks (OCR, image analysis). For policy/compliance moderation of explicit content, Content Safety is the correct service.

D. Azure AI Studio – general hub for building generative AI apps; not the specialized moderation experience.

Microsoft References

Azure AI Content Safety overview and image moderation concepts.

Content Safety Studio quickstart and scenarios.

Content categories (Sexual, Hate, Violence, Self-harm).

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.

Scenario Recap:

You provision a QnA Maker service in a new resource group (RG1) with an App Service plan (AP1).

What gets created automatically?

When provisioning QnA Maker:

Azure App Service (to host the QnA Maker endpoint).

Azure Cognitive Search (to store and index the knowledge base).

Azure Storage (for logs, intermediate data).

Azure SQL Database (for storing KB content in structured form).

However, only two are mandatory/automatic in the scope of the exam context.

Azure App Service (E)

Azure Cognitive Search (D)

Correct Answer: D. Azure Cognitive Search and E. Azure App Service

Comprehensive Detailed ExplanationThe screenshot shows a Bot Framework Composer dialog that collects user input:

user.name is an entity.

False.

user.name is a property in bot state (a variable), not an entity.

Entities are part of LUIS/NLU models (e.g., PersonName, DateTime).

The dialog asks for a user name and a user age and assigns appropriate values to the user.name and user.age properties.

True.

The dialog explicitly has:

A Bot Asks (Text) step → assigns user’s response to user.name.

A Bot Asks (Number) step → assigns response to user.age.

So the values are stored in those properties.

The chatbot attempts to take the first non-null entity value for userName or personName and assigns the value to user.name.

True.

On the right-hand side, the Value expression is:

=coalesce(@userName, @personName)

coalesce returns the first non-null value.

This means if userName entity is available, it is used; otherwise, personName is used.

That result is assigned to user.name.

Correct Answers:

No

Yes

Yes

Bot Framework Composer - Manage variables and properties

Bot Framework Composer - Recognizers and entities

Coalesce function in Composer expressions

Microsoft References

Comprehensive Detailed ExplanationYou’re adding multiple images for each person into a PersonGroup using the Face API SDK.

File.OpenRead(imagePath) returns a Stream, so the using declaration must declare Stream t = File.OpenRead(imagePath).

To upload an image from a stream to a person in a person group, callfaceClient.PersonGroupPerson.AddFaceFromStreamAsync(personGroupId, personId, t);

Putting it together:

Parallel.For(0, PersonCount, async i =>

{

Guid personId = persons[i].PersonId;

string personImageDir = $"/path/to/person/{i}/images";

foreach (string imagePath in Directory.GetFiles(personImageDir, "*.jpg"))

{

using (Stream t = File.OpenRead(imagePath))

{

await faceClient.PersonGroupPerson.AddFaceFromStreamAsync(

personGroupId, personId, t);

}

}

});

This loops the images for each person, opens each as a stream, and adds the face to the specified person in the person group.

Microsoft Docs – Face API: Add a face to a person (AddFaceFromStreamAsync, AddFaceFromUrlAsync) in PersonGroupPerson operations.https://learn.microsoft.com/azure/ai-services/face/ (see SDK/PersonGroupPerson methods)

.NET File.OpenRead returns Stream.https://learn.microsoft.com/dotnet/api/system.io.file.openread

References

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