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Microsoft AI-200 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop AI solutions using Azure data services | 30% | - Implement vector-enabled databases
|
| Topic 2: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Topic 3: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
| Topic 4: Develop containerized AI solutions on Azure | 25% | - Monitor and troubleshoot containerized workloads
|
Microsoft Developing AI Cloud Solutions on Azure Sample Questions:
You have an Azure web app that uses Azure Cosmos DB as a data store. You create a Cosmos DB container by running the following PowerShell script:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The question maps directly to the AI-200 objective "Develop AI solutions by using Azure Cosmos DB for NoSQL," which includes running queries and optimizing query performance and Request Unit (RU) consumption.
Statement 1: No - The minimum throughput is not 400 RU/s.
The PowerShell command provisions the container with:
-AutoscaleMaxThroughput 5000
Azure Cosmos DB autoscale operates between approximately 10% and 100% of the configured maximum throughput . Microsoft documentation specifically gives the example of an autoscale container provisioned with 5,000 RU/s scaling between 500 RU/s and 5,000 RU/s . Therefore, for this container, the minimum operating autoscale throughput is 500 RU/s , not 400 RU/s.
Therefore:
"The minimum throughput for the container is 400 RU/s." # No
Statement 2: No - The first query is not an in-partition query.
The container uses:
/EmployeeId
as its partition key.
The first query is:
SELECT * FROM c WHERE c.EmployeeId > ' 12345 '
Although the query references the partition key, it uses a range predicate ( > ) , not an equality predicate.
Microsoft explicitly states that a range filter on a partition key is not scoped to a single physical partition .
To qualify as an in-partition query, the filter must identify the applicable partition, typically through an equality predicate such as:
WHERE c.EmployeeId = ' 12345 '
Microsoft ' s documentation provides essentially the same example: a query using DeviceId > ... against a container partitioned by DeviceId is not an in-partition query .
Therefore:
"The first query statement is an in-partition query." # No
Statement 3: Yes - The second query is a cross-partition query.
The second query is:
SELECT * FROM c WHERE c.UserId = ' 12345 '
The container ' s partition key is /EmployeeId, not /UserId. Because the query contains no filter on the partition key , Azure Cosmos DB cannot route it to one logical partition. It must fan out the query across the applicable physical partitions and combine the results.
Microsoft describes this behavior directly: when a query does not contain a filter on the partition key, it must execute across the partitions.
Therefore:
"The second query statement is a cross-partition query." # Yes
A semantic search application queries Azure Database for PostgreSQL and stores document embeddings and metadata in a table with the following columns:
* embedding (pgvector)
* department
* created_at
The application must return the top five most similar documents for a given query embedding only from the finance department. You need to implement semantic retrieval with metadata filtering.
Which query components should you select? To answer, move the appropriate query components to the correct requirements. You may use each query component once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
Correct Answer:
Explanation:
Verified Answer: Filter: `WHERE department = ' finance ' `. Rank/top five: `ORDER BY embedding < = > query_embedding LIMIT 5`.
Detailed Explanation: The metadata predicate must restrict the candidate rows to the Finance department, so the `WHERE department = ' finance ' ` component supplies the required filter. The pgvector cosine-distance operator ` < = > ` orders rows by vector distance to the supplied query embedding, and `LIMIT 5` keeps only the five nearest matches. Ordering by creation date would rank recency rather than semantic similarity, and a wildcard department filter would not satisfy the Finance-only requirement.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector similarity search with Azure PostgreSQL
You need to implement trace correlation according to the business requirements.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you se lect.
Correct Answer:
Explanation:
Verified Answer: 1) Instrument the application code with the OpenTelemetry SDK; 2) configure an Azure Monitor trace exporter in the OpenTelemetry SDK; 3) redeploy the instrumented services.
Detailed Explanation: Trace correlation requires the application to create OpenTelemetry spans and an exporter to send those spans to Azure Monitor. Instrumentation must therefore be present before the updated service is deployed. The previous Application Insights SDK instrumentation does not satisfy the case-study requirement that all tracing use OpenTelemetry. Configuring an OpenTelemetry view is unrelated to basic distributed-trace export, and calling TrackEvent is an Application Insights SDK pattern rather than the required OpenTelemetry approach.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | Enable Azure Monitor OpenTelemetry
You are building a semantic search feature for a chatbot. You store document embeddings in Redis.
You review the following Python code that connects to Redis and stores an embedding value:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Verified Answer: Yes; No; Yes.
Detailed Explanation: `HSET` stores the binary embedding as a field in the Redis hash keyed by `doc:1`, so the first statement is true. The code does not create a RediSearch/Redis Query Engine vector index or define a vector field schema; merely storing bytes does not enable similarity search, so the second statement is false.
`EXPIRE doc:1 600` sets a 600-second lifetime, which is ten minutes, making the third statement true.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Vector search in Azure Managed Redis
You need to add a custom voice that matches your company ' s brand for a text-to-speech feature, distinct from any standard neural voice.
What should you use?
- A. Azure AI Speech prebuilt neural voices
- B. Azure AI Speech Custom Neural Voice
- C. Azure AI Language sentiment analysis
- D. Azure AI Translator
Correct Answer: B 🗳️
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