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| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Topic 2: Plan and manage Azure AI solutions | - Plan security and compliance requirements - Select appropriate Azure AI services - Monitor and optimize AI solutions |
| Topic 3: Implement Azure AI solutions | - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search |
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NEW QUESTION # 130
An Azure Service Bus queue processes AI enrichment jobs.
Some messages fail repeatedly because the payload is malformed.
You need to ensure that repeatedly failing messages do NOT block valid messages and can be inspected separately.
Which message action should you perform?
Answer: A
Explanation:
Use the dead-letter action. Azure Service Bus provides a dedicated dead-letter queue (DLQ) for each queue and subscription. Its purpose is to isolate messages that cannot be successfully processed, including malformed or poison messages, so that they do not continue cycling through the active queue and interfering with valid workload processing. Microsoft explicitly identifies malformed or unexpected payloads as a typical poison-message scenario suitable for the DLQ.
When an application explicitly dead-letters a message, the message is moved to the queue ' s dead-letter subqueue where it can be inspected, diagnosed, corrected, or resubmitted later. The dead-letter operation can also include a reason and error description, which improves operational troubleshooting.
Complete removes a successfully processed message permanently, so it is inappropriate for a failed message that must be retained. Abandon releases the message lock and makes the message available for redelivery, which would perpetuate repeated failures. Defer postpones processing but leaves the message associated with the main entity and requires its sequence number for later retrieval; it is not the standard mechanism for isolating poison messages.
Therefore, the correct action is dead-letter .
Study Guide references: Azure Service Bus # message settlement; dead-letter queues; poison-message handling; DeadLetter operation.
NEW QUESTION # 131
You develop an AI application that stores telemetry documents in Azure Cosmos DB for NoSQL.
Each document contains 30 properties. Queries filter only on deviceId and timestamp.
Write operations consume more RUs than expected.
You need to reduce RU consumption for write operations while maintaining query performance.
What should you do?
Answer: B
Explanation:
Modify the indexing policy to include only deviceId and timestamp paths, and exclude all other paths. By default, Azure Cosmos DB indexes every property in a document, which drastically inflates the Request Units (RUs) consumed during write operations.
Reference:
https://www.datacamp.com/tutorial/azure-cosmos-db
NEW QUESTION # 132
You are developing a Retrieval-Augmented Generation (RAG) solution for a company. AI responses and embedding vectors are cached in Redis. The solution must meet the following requirements.
* AI responses must expire exactly 24 hours after they are cached
* Cached embeddings must always reflect the current source data.
You need to configure Redis to meet the requirements.
Answer:
Explanation:
Explanation:
I response expiry: set a 24-hour TTL on each key. Embedding freshness: delete related cache keys when the source document changes.
Detailed Explanation: A fixed TTL encodes the requirement that an AI response must expire after a defined period rather than survive indefinitely or have its lifetime extended on every read. Embedding freshness is a separate consistency requirement: when the authoritative document changes, any cached representation derived from the old document must be invalidated immediately. Memory eviction policies are capacity- management mechanisms and cannot guarantee a precise 24-hour lifetime or source-data consistency.
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 | Azure Managed Redis documentation
NEW QUESTION # 133
You need to address the known issue resulting from vector similarity queries.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Detailed Explanation: The objective is to reduce RU consumption from vector similarity queries. Microsoft guidance identifies vector numeric precision and vector-index selection as major cost and performance levers.
Using a lower supported vector precision can reduce storage and processing cost, while quantizedFlat and DiskANN are designed to reduce latency and RU consumption compared with flat search for appropriate data sizes. Strong consistency would increase resource cost rather than solve vector-search efficiency. A regular composite index is not a substitute for the vector index. Option B should be read as reducing vector numeric precision, not changing a generic "indexing precision" setting.
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 Cosmos DB | Optimize Cosmos DB vector search performance
NEW QUESTION # 134
A large retail company operates online and physical stores. The company tracks inventory levels in real time to manage stock efficiently across all locations. You develop an Azure Event Grid solution to handle events generated by the inventory management system deployed to Azure.
You need to implement a subscription filter that dynamically adjusts to seasonal changes in product demand.
Which event filter should you use?
Answer: A
NEW QUESTION # 135
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