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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Develop containerized AI solutions on Azure | 25% | - Implement container hosting environments
|
| Topic 2: Integrate backend services and build event-driven architectures | 25% | - Build serverless APIs and workflows
|
| Topic 3: Develop AI solutions using Azure data services | 30% | - Design and optimize data access and retrieval
|
| Topic 4: Secure, monitor, and optimize AI solutions | 20% | - Implement observability and reliability
|
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NEW QUESTION # 37
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?
Answer: C
Explanation:
Azure AI Search's semantic ranker re-scores the initial retrieval results using a secondary language-understanding model, improving precision by pushing more contextually relevant results to the top without any custom model training.
NEW QUESTION # 38
You are reviewing secret access patterns used by an AI application that retrieves credentials from Key Vault.
You need to evaluate the security impact of each implementation approach.
What is the outcome of each approach? To answer, move the appropriate outcomes to the correct implementations. You may use each outcome 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:
NEW QUESTION # 39
A customer support bot built with Azure AI Foundry Agent Service must ground its answers strictly in an internal knowledge base and avoid hallucinating unsupported facts.
What should you implement?
Answer: C
Explanation:
Connect the agent to an Azure AI Search knowledge source and explicitly constrain its instructions to answer from retrieved evidence. Microsoft Foundry Agent Service can use Azure AI Search to retrieve proprietary organizational content and ground model responses in that content. Retrieved documents provide the factual context from which the model generates its answer, and Azure AI Search can return source references for attribution.
Microsoft ' s RAG guidance also specifically recommends using clear system instructions that require the model to stay within retrieved content when hallucination risk must be reduced. If the available evidence does not support an answer, the instructions should direct the agent to state that the information is unavailable rather than inventing facts. Retrieval grounding substantially reduces unsupported generation, although production systems should still evaluate outputs because grounding alone does not mathematically guarantee zero hallucinations.
Increasing temperature would generally increase output variability and is contrary to the objective. A larger context window without retrieval does not give the model access to the organization ' s internal knowledge base. Disabling content filtering weakens safety controls and has no role in factual grounding.
Study Guide references: Microsoft Foundry Agent Service # Azure AI Search tool; RAG grounding; knowledge sources; source attribution; grounding instructions.
NEW QUESTION # 40
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. Choose three.
NOTE: Each correct selection is worth one point.
Answer: A,B,C
NEW QUESTION # 41
You need to implement trace correlation according to the business requirements.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you se lect.
Answer:
Explanation:
Explanation:
Verified Answer: 1) Instrument the application code with the OpenTelemetry SDK; 2) configure an Azure Monitor trace exporter in the OpenTelemetry SDK; 3) redeploy the instrumented services.
Detailed Explanation: Trace correlation requires the application to create OpenTelemetry spans and an exporter to send those spans to Azure Monitor. Instrumentation must therefore be present before the updated service is deployed. The previous Application Insights SDK instrumentation does not satisfy the case-study requirement that all tracing use OpenTelemetry. Configuring an OpenTelemetry view is unrelated to basic distributed-trace export, and calling TrackEvent is an Application Insights SDK pattern rather than the required OpenTelemetry approach.
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 | Enable Azure Monitor OpenTelemetry
NEW QUESTION # 42
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