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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. You deploy an AI application across multiple Azure regions.
The application must be able to view writes across all regions within a predictable time window.
You need to determine the appropriate consistency level.
What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) Eventual
B) Session
C) Strong
D) Bounded staleness

2. A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?

A) Decrease the initialDelaySeconds for the container liveness probe.
B) Drain and reboot the node hosting the pod.
C) Inspect the pod events and container logs.
D) Scale the deployment to more replicas.

3. You deploy a GPT-4o model in Azure AI Foundry. Users report inconsistent completions for the same prompt. You need to make outputs more deterministic without changing the prompt. What should you do?

A) Set temperature to a low value such as 0
B) Increase the top_p parameter
C) Enable content filtering
D) Increase the max_tokens parameter

4. You are developing an Azure Function that calls external APIs by providing an access token for the API. The access token is stored in a secret named token in an Azure Key Vault named mykeyvault. You need to ensure the Azure Function can access the token. Which value should you store in the Azure Functions app configuration?

A) KeyVault:mykeyvault;Secret:token
B) @Microsoft.KeyVault(SecretUri=https://mykeyvault.vault.azure.net/secrets/token/)
C) App:Settings:Secret:mykeyvault:token
D) AZUREKVCONNSTR_ https://mykeyvault.vault.azure.net/secrets/token/

5. You are choosing an embedding strategy for a RAG solution. Documents range from 2 to 200 pages. You need to preserve semantic coherence while staying within embedding model token limits. What should you do?

A) Chunk documents into smaller overlapping segments before embedding
B) Use only document titles for embedding
C) Convert documents to images and use image embeddings
D) Embed each entire document as a single vector

Solutions: