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Microsoft AI-200 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing AI Cloud Solutions on Azure |
| Exam Number: | AI-200 |
| Real Exam Qty: | 40–60 |
| Available Languages: | English, Chinese (Simplified), Spanish, Japanese, Portuguese (Brazil), German, Korean, French |
| Exam Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Passing Score: | 700 (out of 1000) |
| Certificate Validity Period: | 1 year, renewable free via online assessment |
| Related Certifications: | Microsoft Certified: Azure Developer Associate (retiring July 31, 2026) |
| Exam Format: | Multiple choice, Case studies, Scenario-based questions |
| Recommended Training: | AI-200T00: Developing AI Cloud Solutions on Azure |
| Exam Registration: | Pearson VUE Scheduling Microsoft Certification Registration |
| Sample Questions: | Microsoft AI-200 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | Basic programming experience (Python, .NET, JavaScript, or similar); familiarity with Azure development, containers, and cloud architectures |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-200 |
Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Topic 2: Integrate backend services and build event-driven architectures | 25% | - Implement messaging and event systems
|
| Topic 3: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
You need to address the known issue resulting from vector similarity queries.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
- A. Change the vector index type from flat to quantizedFlat or diskANN.
- B. Add a composite index on the vector fields and metadata properties of the container.
- C. Set the account consistency level to Strong.
- D. Reduce the numeric precision of the vector embeddings where supported.
Correct Answer: A,D 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You need to configure the database resources for the Azure Database for PostgreSQL instance.
How should you complete the configuration to meet the business and technical requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Meet the 200-ms semantic search latency requirement: Increase compute vCores.
* Optimize the environment for high-dimensional pgvector index residency: Increase memory allocation.
* Support the continuous ingestion of transaction-based embeddings: Enable storage autoscale.
For the strict sub-200-ms vector-search latency target, increasing compute vCores is the appropriate choice.
Vector similarity operations are computationally intensive, and additional CPU capacity improves mathematical throughput and parallel query execution. Microsoft's pgvector guidance emphasizes query-plan optimization, ANN indexes such as HNSW, and sufficient compute resources when optimizing vector workloads.
For high-dimensional pgvector index residency , increase memory allocation . HNSW provides strong query-performance characteristics but consumes more memory than IVFFlat. Keeping frequently accessed vector index structures in memory minimizes disk access and materially improves latency. Microsoft explicitly notes that HNSW requires more memory while providing a better speed/recall tradeoff.
For the continuous ingestion of millions of embeddings, enable storage autoscale . Azure Database for PostgreSQL Flexible Server can automatically increase allocated storage as capacity approaches configured thresholds, avoiding an out-of-storage condition as data volumes grow. Microsoft recommends storage autogrow for workloads whose storage demand can increase dynamically.
Increasing max_connections does not directly improve vector computation or index residency, read replicas primarily scale reads, and backup retention does not address ingestion capacity.
Study Guide references: Azure Database for PostgreSQL Flexible Server # pgvector performance optimization; compute and memory sizing; HNSW indexing; storage autogrow.
You store embeddings in a property named embedding in an Azure Cosmos DB for NoSQL container.
You must construct a vector similarity query that orders documents by similarity to the input embedding and returns only the top five results.
You need to construct the similarity query.
What should you do?
- A. Group by embedding value.
- B. Limit the number of results.
- C. Use offset pagination.
- D. Select distinct embedding.
Correct Answer: B 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
You need to troubleshoot connectivity failures between microservices running in AKS.
Which troubleshooting actions should you perform? To answer, move the appropriate action to the correct troubleshooting scenario. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
* Service fails to communicate with another service: Inspect Kubernetes service endpoints.
* Pod restarts repeatedly: Inspect container logs.
* Readiness probe failures are observed: Inspect Pod descriptions.
For a service-to-service communication failure , first inspect the Kubernetes service endpoints . Microsoft' s AKS troubleshooting guidance recommends verifying that the destination pod IP address and application port appear as endpoints for the Kubernetes Service. An empty or incorrect endpoint list commonly indicates a selector/label mismatch, incorrect port configuration, or unavailable backend pods.
For a pod that restarts repeatedly , inspect the container logs , including the previous container instance when necessary. Microsoft specifically recommends kubectl logs < pod > --previous for workloads that repeatedly restart because it exposes application errors and termination messages from the container immediately before the restart.
For readiness probe failures , inspect the Pod description by using kubectl describe pod. The description exposes the configured health probes, pod conditions, container state, and associated probe-failure events, allowing you to determine whether the probe path, port, timing, or application readiness behavior is incorrect.
Microsoft recommends reviewing kubectl describe output when pods are not Ready or health probes are failing.
Therefore, the correct mapping is service endpoints # container logs # Pod descriptions .
Study Guide references: AKS troubleshooting; Kubernetes Services and endpoints; container logging; Pod health; readiness/liveness probes; kubectl describe and kubectl logs.
By Vicky
