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Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) (AI-300) Practice Test

181 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

Instant feedback after each answer
Full explanations included
Domain score breakdown
Real exam: 100 min
Pass mark: 700 (scaled 1–1,000)

Sample questions with explanations

This is exactly what you see during practice — question, options, and a full explanation after you answer.

Q1Genaiops Infrastructureeasy
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You are monitoring the cost of your GenAI infrastructure. You want to track usage per specific project. Which feature in Azure AI Foundry should you use?

Use 'Resource Tags' on the Azure AI project resource.Correct
BConfigure 'Cost Analysis' alerts in the Azure Portal.
CImplement 'Azure Policy' to restrict resource types.
DSwitch to 'Pay-As-You-Go' subscription model.

Resource Tags on the Azure AI project resource allow you to categorize and track costs by project. By applying tags (e.g., Project: A), you can use Azure Cost Management to filter and analyze costs per tag, enabling per-project cost tracking. This is the recommended approach for …Read full explanation

Q2Genaiops Infrastructurehard
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Your team is using Semantic Kernel for agent orchestration. You notice frequent transient errors when the agent interacts with multiple LLM services. How should you implement infrastructure-level resiliency?

ADisable the 'Streaming' flag in the agent request.
Use an 'Azure API Management' policy to implement a retry pattern.Correct
CSwitch the model to a higher tier instance type.
DIncrease the 'Timeout' parameter in the model deployment manifest.

Using an Azure API Management (APIM) policy to implement a retry pattern is the correct approach for infrastructure-level resiliency. APIM acts as a gateway that can intercept requests to backend LLM services and apply policies such as retry, circuit breaker, and rate limiting. T…Read full explanation

Q3Genaiops Infrastructuremedium
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You need to audit all model access logs across your organization. What is the most effective approach?

AReview individual request headers in the app logs.
Enable 'Diagnostic Settings' to send logs to a 'Log Analytics Workspace'.Correct
CUse 'Azure Advisor' to detect unauthorized access.
DDownload the 'Model Usage' report from the billing console.

Enabling diagnostic settings on the Azure AI Services resource to send logs to a Log Analytics workspace is the standard for organization-wide auditing.

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