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Microsoft AI-200 Exam Syllabus Topics:

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

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

2. A RAG application's retrieval step is returning documents that are topically related but not precisely relevant to the user's question. You need to improve retrieval precision without retraining any model. What should you do?

A) Increase the embedding model's dimensionality only
B) Disable vector search and use only keyword search
C) Enable semantic ranking (re-ranking) in Azure AI Search
D) Reduce the number of indexed documents

3. You are designing an Azure Function that will process orders from a new Azure Service Bus queue.
You need to prevent the system from processing messages more than once and preserve failed messages for investigation.
Which two actions should you implement? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Include a dead-letter handling process.
B) Enable duplicate detection on the queue.
C) Enable sessions on Service Bus.
D) Process orders by using an HTTP trigger.

4. An application performs similarity search across 5 million embeddings stored in Azure Database for PostgreSQL with pgvector. Queries often filter by department before ranking by cosine distance.
P95 latency for vector similarity queries exceeds the SLA target. Monitoring shows sustained high CPU use during query execution.
You need to reduce P95 latency for filtered vector similarity queries.
What should you do?

A) Increase embedding dimensionality.
B) Increase statement timeout.
C) Store embeddings as JSON.
D) Create B-tree indexes on frequently filtered metadata columns.

5. You must ensure an Azure OpenAI-powered application never exceeds a defined token-per- minute budget across all users to avoid runaway costs. What should you configure?

A) Content filtering severity levels
B) Model temperature
C) A vector index size limit in Azure AI Search
D) Deployment-level rate limits (TPM/RPM) in Azure AI Foundry

Solutions: