You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column. The schema must support vector similarity search and reliable date filtering You need to ensure that the table meets the requirements.
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. Store embeddings in a varchar column.
• B. Store embeddings in a pgvector column.
• C. Use a typed timestamp column for created_at.
• D. Store created_at as a free-form string.

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You are reviewing an Azure Function app that processes incoming order requests for a company. The function must:
* Accept order submissions from an external client application.
* Require controlled access for security
* Return a response containing the processed request payload.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation:

Verified Answer: Yes; No; Yes; No.
Detailed Explanation: `auth_level=FUNCTION` means invocation requires a function key unless a stronger platform authentication layer is configured, so the first statement is true. The route explicitly permits POST, not GET. The function reads `req.get_body()` and returns that value in the HttpResponse, so the response contains the request body. No schema, type, required-field, or other payload validation is performed before the response is constructed, making the final statement false.
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 | Azure Functions HTTP trigger

You need to configure the Redis integration for the Recommendation API.
Which configurations should you use? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.

Explanation:
* Comply with the defined expiration policy for recommendation results: Configure Time to Live on each cache key.
* Support the data integrity and freshness requirement for catalog updates: Publish invalidation events to a Redis channel.
* Meet the search latency target for recurring product requests: Implement cache-aside pattern with lazy loading.
The 10-minute expiration requirement should be implemented by configuring a Time to Live (TTL) on each Redis cache key. Redis automatically removes a key after its assigned expiration interval, ensuring recommendation results cannot remain cached indefinitely. Microsoft documents expiration as a standard mechanism for preventing cached data from becoming stale.
For catalog changes, Fabrikam explicitly requires a reactive invalidation mechanism . Publishing invalidation events to a Redis Pub/Sub channel allows application components to notify subscribers immediately when product metadata or pricing changes. Redis Pub/Sub broadcasts messages to channel subscribers, which can then invalidate affected cache entries instead of waiting for TTL expiration.
For repeated recommendation queries, implement the cache-aside pattern with lazy loading . The application first checks Redis; on a cache hit, results are returned from the low-latency in-memory cache. On a miss, the application retrieves data from PostgreSQL, places the result in Redis, and returns it. Microsoft identifies cache-aside as an appropriate pattern for frequently read data and explicitly notes that Azure Managed Redis provides low-latency, high-throughput access.
Redis RDB persistence is unnecessary for these requirements because the cache is an optimization layer rather than the authoritative data store.
Study Guide references: Azure Managed Redis # cache-aside pattern; TTL/expiration; Pub/Sub; cache invalidation and data freshness.

You are implementing semantic retrieval for a chatbot.
Embeddings are already stored in Redis. However, vector similarity queries do not return matches.
You need to resolve the vector similarity search issue.
What should you do?

• A. Set a 24-hour Time to Live (TTL) on embedding keys.
• B. Reset expiration on each read.
• C. Create an HNSW vector index on the embedding field.
• D. Enable geo-replication.

You are developing an AI-powered API that retrieves connection strings and API keys from Azure Key Vault.
You must configure a solution that provides the following security functionality:
* The API must authenticate to Key Vault without storing credentials in any application configuration files
* The identity used by the API must have only the minimum permissions necessary to read secrets.
* The configuration must minimize the blast radius if an identity or credential is compromised.
You need to implement a secure access strategy for the API.
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. Assign the Key Vault Administrator role at subscription scope.
• B. Store a secret value in Azure App Configuration.
• C. Use system assigned managed identity.
• D. Grant the Key Vault Secrets User role at vault scope

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