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| Section | Objectives |
|---|---|
| Implement and monitor AI workloads | - Monitor performance and troubleshoot issues - Deploy AI models and services |
| Implement Azure AI solutions | - Implement computer vision solutions - Implement natural language processing solutions - Implement generative AI solutions using Azure OpenAI - Implement knowledge mining with Azure AI Search |
| Plan and manage Azure AI solutions | - Select appropriate Azure AI services - Monitor and optimize AI solutions - Plan security and compliance requirements |
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NEW QUESTION # 131
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 # 132
You are designing an Azure Database for PostgreSQL table for semantic search. Queries frequently filter on the created_at column.
The schema must support vector similarity search and reliable date filtering.
You need to ensure that the table meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
NEW QUESTION # 133
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: D
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 # 134
You are designing an Azure Function app that exposes a public API.
The solution must:
Validate incoming request data and return results immediately to the caller.
Support Microsoft Entra ID authentication.
Scale automatically under variable load.
You need to implement a trigger.
Which trigger should you implement?
Answer: C
Explanation:
Detailed Explanation: An HTTP trigger is the only option that directly accepts a synchronous external request and can return a response immediately to the caller. Azure Functions uses HTTP triggers to implement serverless APIs and webhooks, while Microsoft Entra authentication can be applied to the Function App or API surface. Queue, Event Grid, and Service Bus triggers are asynchronous event-processing mechanisms and do not provide the request/response semantics required by this public API scenario.
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 | Azure Functions HTTP trigger
NEW QUESTION # 135
You develop a message-processing service deployed to Azure Container Apps. The service reads messages from an Azure Service Bus queue.
The solution must minimize costs by ensuring NO compute resources are consumed when the queue is empty.
You need to configure scaling for the service.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Detailed Explanation: Azure Container Apps uses KEDA-based scale rules for event-driven workloads. A Service Bus scaler can monitor queue depth and create replicas only when messages are pending. To meet the explicit cost requirement, the scale configuration must permit a minimum replica count of zero so all active replicas can terminate when the queue is empty. Increasing the maximum replica count changes peak capacity, and HTTP ingress concurrency is not the correct scaling signal for a background Service Bus consumer.
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 | Set scaling rules in Azure Container Apps
NEW QUESTION # 136
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