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| Section | Objectives |
|---|---|
| Topic 1: Implement and monitor AI workloads | - Deploy AI models and services - Monitor performance and troubleshoot issues |
| Topic 2: Implement Azure AI solutions | - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions |
| Topic 3: Plan and manage Azure AI solutions | - Monitor and optimize AI solutions - Select appropriate Azure AI services - Plan security and compliance requirements |
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NEW QUESTION # 126
You deploy a Linux container image to App Service.
The container requires the following environment variables at runtime:
* A non-sensitive configuration value named MODEL_VERSION
* A database password that must remain secure
You need to configure App Service to provide these environment variables at runtime.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 127
Your Azure AI Search index must support both exact keyword matches on product SKUs and semantic similarity search on product descriptions in a single query. What should you configure?
Answer: A
Explanation:
Hybrid search in Azure AI Search combines traditional keyword (BM25) search over text fields with vector similarity search over embedding fields in a single query, giving both exact-match precision (SKUs) and semantic recall (descriptions).
NEW QUESTION # 128
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?
Answer: B
NEW QUESTION # 129
You deploy multiple instances of a change feed processor to handle a high ingestion rate within Azure Cosmos DB for NoSQL.
Each processor instance must process a different subset of partitions.
You need to ensure the workload is load-balanced across all processor instances.
What should you configure?
Answer: B
Explanation:
The lease container is the Azure Cosmos DB component that coordinates change feed processing across multiple processor instances. Microsoft defines the lease container as the state store used by the change feed processor to track progress and coordinate ownership of partition ranges among workers. Each lease corresponds to a portion of the change feed workload, and at any given time a lease is owned by one processor instance.
When several change feed processor instances use the same lease container and the same processor name while maintaining unique instance names, the processor distributes leases across the active instances by using an equal-distribution algorithm. If instances are added or removed, the processor dynamically redistributes leases, which provides automatic workload balancing and parallelism. This is exactly what is required when ingestion volume is high and each instance must process a distinct subset of partition ranges.
Indexing precision affects query indexing behavior, not change feed coordination. Strong consistency controls read consistency semantics and does not assign partitions to processors. Autoscale throughput can increase database RU/s capacity, but it does not coordinate which processor instance owns which change feed partition range.
Study Guide references: Azure Cosmos DB for NoSQL # Change Feed Processor; lease container; parallel processing; dynamic scaling and load balancing.
NEW QUESTION # 130
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: A
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 # 131
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