Microsoft AI-200 Dumps Questions Answers

Verified Answer: 1) Select the requests table; 2) apply the 24-hour time filter; 3) filter failed requests; 4) join the dependencies table; 5) summarize average dependency duration by operation.

Detailed Explanation: KQL performs best when high-selectivity filters are applied as early as possible, especially the time predicate that limits the amount of data scanned. Starting from AppRequests, restricting to the last 24 hours, and then filtering failures reduces the left-side dataset before the join. The dependency table is then correlated with those requests, and aggregation is performed last to calculate average dependency duration by operation. Joining or summarizing before the time and failure filters would process more data than necessary.

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 | Optimize log queries in Azure Monitor

Verified Answer: Modify SQL: use a parameterized query. Bind input: pass the ID as an argument. Execute: supply the parameter tuple to the SDK/driver method.

Detailed Explanation: The security boundary is created by separating SQL syntax from user-supplied values. The statement should contain a parameter placeholder rather than concatenated input, and the document ID should be supplied separately through the database driver’s parameter mechanism. The driver then performs the correct protocol-level binding and escaping. Manually escaping quotation marks is error-prone and does not provide the same injection resistance as true parameterization.

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

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