An application performs similarity search across 5 million embeddings stored in Azure Database for PostgreSQL with pgvector. Queries often filter by department before ranking by cosine distance.
P95 latency for vector similarity queries exceeds the SLA target. Monitoring shows sustained high CPU use during query execution.
You need to reduce P95 latency for filtered vector similarity queries.
What should you do?

• A. Store embeddings as JSON.
• B. Create a pgvector index on the embedding column.
• C. Increase statement timeout.
• D. Remove the similarity ORDER BY clause.

You are developing several microservices to deploy to a new Azure Kubernetes Service cluster The microservices manage data stored m Azure Cosmos DB and Azure Blob storage. The data is secures by using customer managed keys stored in Azure Key Vault.
You must automate key rotation for all Azure Key Vault keys and allow for manual key rotation. Keys must rotate every three months. Notifications of expiring keys must be sent before key expiry.
You need to configure key rotation and enable key expiry notifications.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. Configure Azure Key Vault alerts.
• B. Create and configure a new Azure Event Grid instance
• C. Create and assign an Azure Key Vault access policy
• D. Create and configure a key rotation policy during key creation.

A RAG application ' s retrieval step is returning documents that are topically related but not precisely relevant to the user ' s question.
You need to improve retrieval precision without retraining any model.
What should you do?

• A. Enable semantic ranking (re-ranking) in Azure AI Search
• B. Disable vector search and use only keyword search
• C. Increase the embedding model ' s dimensionality only
• D. Reduce the number of indexed documents

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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.

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.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals.
You are using Azure Monitor Application Insights to investigate a production API. You open the Logs blade and set the time range to Last 24 hours.
An engineer recommends the following query to count requests by result code and sort the results from most frequent to least frequent:
requests
| summarize request_count = count() by resultCode
| order by request_count desc
You need to determine whether the query returns the number of requests grouped by result code and sorted from most to least frequent.
Solution: The result codes are sorted alphabetically.
Does the solution meet the goal?

• A. Yes
• B. No

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