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| Section | Objectives |
|---|---|
| Topic 1: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
| Topic 2: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 3: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 4: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
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NEW QUESTION # 82
You need to ensure that responses from your Azure OpenAI application include citations back to the specific source documents used, to support user trust and verification.
What should you implement?
Answer: C
Explanation:
A retrieval-augmented generation (RAG) architecture is the correct mechanism when generated answers must be traceable to specific source documents. RAG first retrieves relevant document chunks from a search index or other knowledge source, supplies those chunks to the language model as grounding context, and then generates an answer based on that retrieved information. Microsoft specifically documents that RAG enables responses containing citations back to source content .
For reliable citation generation, each retrieved chunk should carry identifying metadata such as a source identifier, document title, section, page, or URL . Microsoft's Azure RAG guidance recommends labeling retrieved chunks and including source metadata with them, then explicitly instructing the model to cite the corresponding sources in its response. This allows users to verify individual claims against the underlying documents.
Modern Azure AI Search agentic retrieval can also return structured references and grounding metadata , specifically designed for citation linking.
Increasing top_p only alters token sampling. Content filtering addresses safety, not provenance. Using a base model without retrieval provides no authoritative document linkage.
Study Guide references: RAG architecture; grounding data; retrieval metadata; Azure AI Search references; prompt engineering for citations.
NEW QUESTION # 83
You are developing a microservices-based application that uses Azure Container Apps The application consists of several containerized services that handle tasks, such as processing orders, managing inventory, and generating reports.
You must secure the container apps. All apps must reside in the same virtual network, share the same Dapr configuration, and share the same logging location.
Apps must support the configuration of the amount of memory and compute resources available to containers.
You need to configure the Azure Container App
How should you complete the CLI command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Verified Answer: `az containerapp env create ... --enable-workload-profiles`.
Detailed Explanation: The shared boundary for Container Apps is the managed environment. Apps in one environment can share the environment's virtual network integration, observability destination, and Dapr- related environment configuration. Enabling workload profiles provides selectable compute profiles and resource sizing options for workloads in that environment. The CLI must therefore create a Container Apps environment and enable workload profiles. Creating an individual container app alone would not establish the common environment-level network, logging, and resource-profile boundary required by the scenario.
Study Guide Alignment: Containerized Azure workloads: registry builds, App Service containers, Container Apps revision/scaling behavior, and AKS deployment choices.
Official Microsoft Learn References: AI-200 Study Guide | Azure Container Apps environments
NEW QUESTION # 84
You are configuring sampling for a distributed application that sends traces to Azure Monitor. The solution must:
* Preserve upstream sampling decisions across distributed traces
* Capture all spans during local testing.
* Sample 10 percent of traces in production.
You need to apply the appropriate sampling configuration for each requirement.
What should you do? 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.
Answer:
Explanation:
Explanation:
Verified Answer: Preserve parent sampling decisions: ParentBasedSampler. Capture all spans locally:
AlwaysOnSampler. Sample 10% in production: TraceIdRatioBasedSampler(0.1).
Detailed Explanation: Parent-based sampling propagates the sampling decision from the upstream parent, preventing broken sampling decisions across a distributed trace. AlwaysOnSampler records every span, which is suitable for local test environments where complete trace visibility is more important than telemetry volume. TraceIdRatioBasedSampler with 0.1 selects approximately ten percent of traces based on trace identifiers, providing deterministic fixed-ratio sampling for production. A batch span processor and an exporter control processing/export, not the sampling decision itself.
Study Guide Alignment: Security and operations: Key Vault, App Configuration, managed identity, OpenTelemetry, Azure Monitor, and KQL-based troubleshooting.
Official Microsoft Learn References: AI-200 Study Guide | OpenTelemetry sampling in Azure Monitor | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 85
You are implementing semantic retrieval in Redis.
The solution must support low-latency, approximate nearest neighbor (ANN) vector similarity search for large-scale AI retrieval workloads.
You need to select the appropriate vector schema settings.
Which configuration values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 86
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: C
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 # 87
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