AI-102 Exam Dumps - Microsoft Azure AI Engineer Associate Questions and Answers

In user want to change to deploy offline model

1. Change model domain to compact model

2. Retrain compact model

3. Export model

You are developing an app that uses the Azure AI Speech service , and you must ensure the app can authenticate using a Microsoft Entra ID (Azure AD) token , rather than the standard key-based authentication.

According to the official Microsoft documentation :

“To authenticate with a Microsoft Entra token, the Speech resource must have a custom subdomain and use a private endpoint. The Speech service uses custom subdomains with private endpoints only.”

— Microsoft Learn: Azure AI Speech service - Role-based access control

Therefore, two prerequisites must be configured for Entra ID token-based authentication:

1. Create (Certify) a Custom Subdomain (Option D)

When you create an Azure AI Speech resource, you can specify a custom subdomain name (e.g., contoso.cognitiveservices.azure.com).

The custom subdomain ensures that each Speech endpoint has a unique URL.

Microsoft Entra token authentication depends on this subdomain for token validation.

Without a custom subdomain, Entra ID tokens cannot be used for authentication.

Hence, this is required.

2. Create a Private Endpoint (Option B)

For Microsoft Entra ID authentication, the Speech resource must be secured within a private endpoint .

Private endpoints provide a secure, private connection from your virtual network to the Azure AI Speech resource.

The Speech service only supports Entra ID token authentication when the resource is configured with a private endpoint.

Hence, this is required.

Why Not the Other Options:

A. Create a Conditional Access – Conditional Access policies restrict user access but do not configure token authentication for services.

C. Request an X.509 certificate – Certificates are not used for Speech service authentication.

E. Enable a virtual network service endpoint – The Speech service uses private endpoints , not service endpoints, for Entra ID token authentication.

According to the course AI-102T00: Designing and Implementing a Microsoft Azure AI Solution1, the correct sequence of actions to run model1 on Host1 is:

Export model1 to Host1

Run the container

Configure disk logging

The explanation and references are as follows:

Export model1 to Host1: This step is required to deploy the custom speech-to-text model to the Docker host. You can use the Azure portal or the Azure CLI to export the model as a container image2.

Run the container: This step is required to start the container and run the model on the Docker host. You can use the Docker CLI to run the container image3.

Configure disk logging: This step is optional but recommended to monitor the performance and health of the container. You can use the Docker CLI to configure disk logging for the container4.

Box 1: Yes

The ActivityHandler.OnMembersAddedAsync method overrides this in a derived class to provide logic for when members other than the bot join the conversation, such as your bot ' s welcome logic.

Box 2: Yes

membersAdded is a list of all the members added to the conversation, as described by the conversation update activity.

Box 3: No

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

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

Therefore:

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

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

Microsoft Azure AI References (titles only)

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

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

Azure AI Agent Service – Tool connections and ToolConnectionList usage

You are using Azure Cognitive Search, where access is controlled by:

Admin keys: Provide full read/write control (never given to apps).

Query keys: Provide read-only access, meant for client apps.

Here, the query key has been compromised. You want to minimize downtime and ensure continued read-only access.

Step 1 – Add a new query key

First, generate a new query key from the Azure portal or via REST API.

This allows you to continue providing read-only access to authorized apps.

Step 2 – Change the app to use the new key

Update the application configuration so that it authenticates requests with the new query key.

This ensures uninterrupted service while preparing to retire the compromised key.

Step 3 – Delete the compromised key

Once the app is using the new key successfully, revoke the compromised query key.

This prevents further unauthorized usage.

Why not other options?

Regenerate primary/secondary admin key: Not necessary since only the query key is compromised, and admin keys give full control.

Change app to use secondary admin key: Insecure (admin keys should never be exposed to client apps).

Correct Sequence

Add a new query key.

Change the app to use the new key.

Delete the compromised key.

Microsoft References

Manage admin and query keys in Azure Cognitive Search

Create and use query keys

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

You are creating an SSML file for a text-to-speech app using a custom neural voice.

Requirements:

Expresses a calm tone → corresponds to style (e.g., " gentle " ).

Imitates the voice of a young adult female → corresponds to role (e.g., " YoungAdultFemale " ).

Key Knowledge:

In Speech Synthesis Markup Language (SSML) with Microsoft Azure TTS:

< mstts:express-as > supports attributes such as style, role, styledegree, voice, etc.

role → controls persona/age/gender (e.g., " YoungAdultFemale " ).

style → controls the speaking tone/intonation (e.g., " gentle " , " calm " , " chat " ).

Code Completion:

< mstts:express-as role = " YoungAdultFemale " style = " gentle " >

How can I assist you?

< / mstts:express-as >

Verified Answer:

First dropdown: role

Second dropdown: style

Microsoft References:

Azure Speech SSML – express-as element

Speech synthesis styles and roles

✅ Final Answer:

role = " YoungAdultFemale "

style = " gentle "

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

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

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

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

Provision the Language service resource in Azure.

Deploy a Docker container to an on-premises server.

Run the container and query the prediction endpoint.

According to the Microsoft documentation, the Language service is a cloud-based service that provides various natural language processing features, such as sentiment analysis, key phrase extraction, named entity recognition, etc. You can provision the Language service resource in Azure by following the steps in Create a Language resource. You will need to provide a name, a subscription, a resource group, a region, and a pricing tier for your resource. You will also get a key and an endpoint for your resource, which you will use to authenticate your requests to the Language service API.

According to the Microsoft documentation, you can also use the Language service as a container on your own premises or in another cloud. This option gives you more control over your data and network, and allows you to use the Language service without an internet connection. You can deploy a Docker container to an on-premises server by following the steps in Deploy Language containers. You will need to have Docker installed on your server, pull the container image from the Microsoft Container Registry, and run the container with the appropriate parameters. You will also need to activate your container with your key and endpoint from your Azure resource.

According to the Microsoft documentation, once you have deployed and activated your container, you can run it and query the prediction endpoint to get sentiment analysis results. The prediction endpoint is a local URL that follows this format: http:// < container IP address > : < port > /text/analytics/v3.1-preview.4/sentiment. You can send HTTP POST requests to this endpoint with your text input in JSON format, and receive JSON responses with sentiment labels and scores for each document and sentence in your input.