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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 knowledge mining with Azure AI Search - Implement natural language processing solutions - Implement computer vision solutions - Implement generative AI solutions using Azure OpenAI |
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44. Frage
A development team wants to compare multiple prompt variations against the same test dataset and visualize which prompt performs best. What should they use in Azure AI Foundry?
Antwort: A
Begründung:
Prompt flow variants allow you to define multiple versions of a prompt/node, run them against the same evaluation dataset, and compare results side-by-side to identify the best-performing variant.
45. Frage
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.
Antwort:
Begründung:
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
46. Frage
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?
Antwort: C
Begründung:
Dead-lettering is the message action you must use to isolate repeatedly failing messages from valid ones in Azure Service Bus.
Isolation: It moves corrupted or malformed messages into a separate sub-queue.
Non-blocking: Valid messages continue processing without getting stuck behind bad data.
Inspection: It allows developers to analyze failed payloads and system errors later.
Reference:
https://azureintegrations.com/2020/03/01/azure-servce-bus-best-practises/
47. Frage
You are reviewing the message processing code in a backend worker service.
You review the following Python code that initializes and starts a Service Bus processor
Antwort:
Begründung:
Explanation:
Verified answer: No; Yes; No.
Detailed Explanation: The default Service Bus receive behavior is PeekLock rather than receive-and-delete, so merely receiving a message does not remove it from the queue; it must be settled successfully. If processing fails and the message remains unsettled or the lock is lost, it can become available for another delivery. Invalid JSON, however, is an application-level content problem: Service Bus does not inspect the payload as JSON and automatically retry it to MaxDeliveryCount solely because deserialization fails. The application's settlement/error path determines what happens.
Study Guide Alignment: Azure service integration: Service Bus, Event Grid, Azure Functions triggers
/bindings, and event-driven processing.
Official Microsoft Learn References: AI-200 Study Guide | Service Bus message transfers, locks, and settlement | Service Bus messages, routing, and correlation
48. Frage
You need to configure vector embedding updates according to the business and technical requirements.
Which information should you use? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Verified Answer: Identify document changes: Change feed processor. Scale out vectorization processing:
Lease container.
Detailed Explanation: The change feed processor is designed to react to inserts and updates in a monitored Cosmos DB container, which directly matches the requirement to generate embeddings for new or changed documents. Its lease container stores processing state and coordinates work across multiple workers, allowing the processing workload to scale out without duplicating ownership of change-feed ranges. A periodic full scan would consume unnecessary RUs, and neither strong consistency nor container RU throughput is the coordination mechanism for distributed change-feed workers.
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 Cosmos DB change feed processor
49. Frage
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