Microsoft AI-102 Korean valid study dumps : Designing and Implementing a Microsoft Azure AI Solution (AI-102 Korean Version)

Correct Answer: C  🗳️

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Correct Answer:

Explanation:
Provide access to AI1 by using: A bearer token
Connect to the deployment by using: A deployment name
To ensure only your apps can access the Azure OpenAI resource, authenticate with Microsoft Entra ID (Azure AD) and obtain a Bearer token (ideally via managed identity ). This avoids sharing long-lived API keys and lets you scope access by app identity and Azure RBAC. Microsoft's guidance recommends token- based auth for Azure OpenAI and shows how to acquire a bearer token/managed identity token for calls When calling the REST API , Azure OpenAI requires you to specify the deployment name to select which model deployment to use. The REST route includes {deployment-id} (deployment name) in the path, e.g., ...
/openai/deployments/{deployment-id}/chat/completions. This is explicitly documented as a key difference from OpenAI's public API, which takes only a model.
Therefore:
Access method: Bearer token (via Entra ID/managed identity)
Deployment selector: Deployment name in the REST path
Microsoft References
Authenticate to Azure OpenAI with Microsoft Entra ID / Managed identity (bearer token): guidance and how- to Azure OpenAI request header recommends token-based authentication (Bearer).
Azure OpenAI requires deployment name in API calls; REST path uses

Correct Answer:

Explanation:

To build narration for video training content using Custom Neural Voice, we must know where each step is done:
Creating a Custom Neural Voice
A Custom Neural Voice is a text-to-speech (TTS) feature within Azure AI Speech.
It allows you to create a unique, natural-sounding synthetic voice.
The correct tool to build and configure a custom voice is the Speech Studio portal.
Not the Language Understanding portal (LUIS), Azure portal directly, or Bot Framework Composer.
Generating narration
Narration requires converting written text into speech.
This is done via the Text-to-Speech service in Azure AI Speech.
Other options:
Language Understanding # For intent/utterance processing, not narration.
Speaker Recognition # For identifying/authenticating speakers, not narration.
Speech-to-text # Converts spoken words to text (the opposite direction).
Thus:
Custom Neural Voice # Speech Studio portal
Narration # Text-to-speech
Correct Answers:
Custom neural voice: The Speech Studio portal
Narration: Text-to-speech
Microsoft References
Custom Neural Voice in Speech Studio
Text-to-Speech overview

Correct Answer: C  🗳️

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Correct Answer:

Explanation:

The requirement:
You are using Azure AI Anomaly Detector.
You need to deploy it on Server1, which has intermittent internet access.
This means you need to run the service locally using containers (Docker).
Step 1 - Install Docker Engine
Before pulling or running any containers, Docker must be installed on Server1.
Without Docker Engine, no container can be deployed.
Step 2 - Pull the container image
Use docker pull to download the Anomaly Detector container image from Microsoft Container Registry.
This requires internet access, so it should be done when connectivity is available.
Step 3 - Run the container
After pulling the image, use docker run to start the Anomaly Detector container.
This exposes a local REST API endpoint on Server1.
Step 4 - Query the local endpoint
Once the container is running, you can query the prediction endpoint on Server1.
This allows local inference without requiring continuous internet access.
Correct Sequence:
Install the Docker Engine on Server1.
From Server1, run the docker pull command.
From Server1, run the docker run command.
Query the prediction endpoint on Server1.
Microsoft References
Use Azure AI Anomaly Detector on containers
Deploy Azure AI services in Docker containers
Anomaly Detector container setup