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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. You maintain multiple versions of a container image in Azure Container Registry.
The production deployment must always run the exact same image build even if tags are changed later.
You need to ensure predictable and immutable image selection during deployment.
What should you do?

A) Configure deployment to use the latest tag.
B) Schedule nightly rebuilds of the image.
C) Tag the image as production and deploy it by using the production tag.
D) Identify the image by using its SHA digest.

2. You need to translate real-time spoken customer conversations from English to Spanish text during a support call, with minimal latency. Which Azure AI service should you use?

A) Azure AI Language (Text Analytics)
B) Azure AI Document Intelligence
C) Azure AI Translator (text translation REST API)
D) Azure AI Speech (Speech Translation)

3. You deploy multiple instances of a change feed processor to handle a high ingestion rate within Azure Cosmos DB for NoSQL.
Each processor instance must process a different subset of partitions.
You need to ensure the workload is load-balanced across all processor instances.
What should you configure?

A) lease container
B) indexing precision
C) autoscale throughput
D) strong consistency

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

5. You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification. What should you implement?

A) Use only the base model with no retrieval
B) Configure the RAG pipeline to return retrieved document metadata (source, page) alongside generated answers and instruct the model to cite them
C) Increase top_p to broaden token sampling
D) Enable higher content filter severity

Solutions: