AI-200 Microsoft Developing AI Cloud Solutions on Azure Free Practice Exam Questions (2026 Updated)

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.

The correct architecture uses an Azure Storage queue as an asynchronous buffer between the HTTP ingestion path and the computationally expensive image-processing workload. The HTTP-triggered function should accept the upload, enqueue a work message, and return promptly rather than waiting for image processing to finish. Microsoft specifically recommends asynchronous patterns for long-running HTTP operations because an HTTP-triggered Azure Function can encounter the Azure Load Balancer response timeout even though the underlying function continues executing.

A queue-triggered function then performs image processing independently. Azure Functions applies target-based scaling to Azure Queue Storage triggers based on queued workload, allowing worker capacity to expand as the backlog increases rather than being directly tied to HTTP arrival processing.

For reliability, Azure Queue-triggered Functions provide retry behavior. The visibilityTimeout setting controls the delay before a failed message becomes available for another attempt, while maxDequeueCount controls how many processing attempts occur before the message is transferred to the < queue-name > -poison queue. The default maximum is five attempts.

C defeats the asynchronous requirement. D can be useful for tracking upload state, but it does not itself provide workload decoupling, retry handling, or queue-driven scaling.

Study Guide references: Azure Functions → Queue Storage triggers and bindings; event-driven scaling; retry/poison queue behavior; asynchronous HTTP processing.

Enable duplicate detection on the Azure Service Bus queue to suppress duplicate sends. Service Bus tracks application-defined MessageId values during the configured duplicate-detection window. If another message with the same MessageId arrives during that interval, Service Bus accepts the send operation but discards the duplicate message. Microsoft specifically recommends using a deterministic business identifier, such as an order ID, as the basis for MessageId when duplicate suppression is required. Duplicate detection must be enabled when the queue is created and is supported in the Standard and Premium tiers.

For failed or poison messages, implement dead-letter handling . A Service Bus queue has a dead-letter subqueue that retains messages that cannot be successfully processed, including messages that exceed the configured maximum delivery count. Those messages remain available for inspection, troubleshooting, correction, or controlled reprocessing rather than being silently discarded.

Sessions provide ordered/FIFO processing and session affinity; they do not primarily prevent duplicates. An HTTP trigger would bypass the required Service Bus processing architecture.

For strict production-grade exactly-once business effects, the consumer should additionally be idempotent , because a message can still be redelivered after a lock or settlement failure.

Study Guide references: Azure Service Bus → duplicate detection; MessageId; dead-letter queues; poison-message handling; reliable asynchronous processing.

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

Verified Answer: 1) Register the Event Grid resource provider; 2) create a custom topic; 3) create an event subscription.

Detailed Explanation: A newly provisioned subscription must have the Event Grid resource provider available before Event Grid resources can be created. The publisher needs a custom topic as the event-ingress resource, and routing to a downstream processor is then defined by an event subscription on that topic. The event subscription can include filters such as event type or data fields so that only high-confidence events reach the processor. Creating a partner topic or domain is unnecessary for the stated custom workflow.

Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers/bindings, and event-driven processing.

Official Microsoft Learn References: AI-200 Study Guide | Create an Event Grid custom topic or domain