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| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Plan security and compliance requirements - Select appropriate Azure AI services |
| Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement computer vision solutions - Implement natural language processing solutions |
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NEW QUESTION # 106
Your organization requires that all prompts and completions sent to Azure OpenAI be retained for zero data logging beyond what's required for abuse monitoring, per contractual requirements.
What should you do?
Answer: C
Explanation:
Customers with qualifying requirements can apply to modify abuse monitoring and human review data logging for an Azure OpenAI resource through Microsoft's formal request process, which is the supported path for controlling this behavior - it isn't a client-side setting.
NEW QUESTION # 107
You configure ACR Tasks to automate image builds.
Container images must rebuild when:
- Application updates occur.
- Base image updates occur, such as when the underlying OS image is
updated.
- Regular scheduled rebuilds are required.
You need to configure ACR Tasks to support automated image rebuilds.
Which three triggers should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D,E
NEW QUESTION # 108
You optimize an AI inference API that uses Redis caching.
You must reduce the risk of serving outdated data while minimizing cache management overhead. You need to implement the caching strategy that satisfies the requirements. What should you do?
Answer: D
Explanation:
Detailed Explanation: If the priority is to minimize stale results, source-driven invalidation is the direct control: when the underlying record changes, delete the corresponding cache entry so the next request repopulates it from current data. Sliding expiration can keep a frequently accessed stale item alive, disabling expiration is worse, and an allkeys-LRU policy evicts according to memory pressure rather than data freshness. Invalidation therefore best addresses correctness without requiring constant cache-wide maintenance.
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 # 109
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?
Answer: D
Explanation:
Creating a B-tree index on the department column will help, but only if you use the correct indexing strategy, as a standard B-tree index alone is often ignored during vector searches.
When you run a query that filters by metadata before performing a vector similarity search, PostgreSQL must choose between filtering the rows first or searching the vector index first. In many cases, a standard B-tree index on the metadata column combined with an HNSW/IVFFlat index on the vector column results in pre-filtering that defaults to a sequential scan, or post- filtering that returns fewer results than requested.
Reference:
https://learn.microsoft.com/en-us/azure/horizondb/ai/vector-search-pgvector
NEW QUESTION # 110
Drag and Drop Question
You are developing a .NET application that uses Azure Cosmos DB for NoSQL to store application data.
The application uses the Azure Cosmos DB for NoSQL SDK to interact with the database account.
The application must perform the following tasks:
- Initialize the connection by using the account endpoint and key.
- Define shared throughput.
- Perform create, read, update, and delete (CRUD) operations on items
stored in a container.
You need to implement the SDK components required for the application to access and manage data in Azure Cosmos DB for NoSQL.
Which SDK components should you use? To answer, move the appropriate components to the correct requirements. You may use each component 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.
Answer:
Explanation:
Explanation:
Box 1: CosmosClient
To initialize the connection to an Azure Cosmos DB for NoSQL account using the account endpoint and key, you must use the CosmosClient class.
Box 2: Database
The database SDK component should be used to define shared throughput.
In Azure Cosmos DB, shared throughput (provisioned Request Units per second or RU/s) is configured at the database level. When throughput is provisioned on a database, that capacity is shared among all the containers created within that specific database.
Box 3: Container
To perform item create, read, update, and delete (CRUD) operations, you should use the Container SDK component In the Azure Cosmos DB for NoSQL SDK for .NET, individual JSON documents (items) live inside a container. The Container class exposes the specific methods required to execute CRUD operations on these items.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/how-to-dotnet-get-started
NEW QUESTION # 111
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