Microsoft AI-300 MLOps and GenAIOps: Applied Practice Tests

Categories: Microsoft Azure
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About Course

This course contains the use of artificial intelligence.

A completed training job may not preserve the correct artifact. A private endpoint may work while its data source remains inaccessible. Prepare for AI-300: Operationalizing Machine Learning and Generative AI Solutions by following production dependencies and quality evidence before approving an AI release.

Operate the model and the application together

Practice Azure Machine Learning infrastructure, training foundations, assets, registries, delivery automation, model lifecycle and monitoring. Microsoft Foundry scenarios examine serving and capacity, prompt versions, evaluation, retrieval, customization and controlled production changes.

The 324 original questions include four 51-question focused sets and two 60-question, 120-minute written readiness mocks. Scenarios combine configuration choices, identity boundaries, metrics and small Python reasoning tasks.

Verify the asset, scope and acceptance criterion

Every question includes option feedback, a condition-based explanation and a study suggestion. Distinguish workspace organization from access isolation, or workspace model registration from organizational registry publication. Examine quality, safety and authorization separately instead of letting a fluent answer satisfy every requirement.

Python, basic data science, Azure resources and Git-based automation knowledge are recommended. No cloud subscription is required for the tests, and a hosted Azure lab is not supplied.

The 60-item forms are author-set: Microsoft does not publish a fixed AI-300 count or exact item allocation. The 80% target does not convert to its scaled score, and selection questions do not reproduce interactive labs. This independent resource has no Microsoft endorsement.

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TRY BEFORE YOU CHOOSE

Three questions. A clearer next step.

Explore original questions and explanations. No account or payment required.

What Will You Learn?

  • Select Azure ML resources, identities, data assets, environments and component contracts for reproducible operations.
  • Evaluate training, feature retrieval, model registration and production deployment decisions using explicit evidence.
  • Apply rollout, rollback, monitoring and retraining gates without confusing job completion with model approval.
  • Choose compatible Foundry serving, networking and capacity configurations and control prompt releases.
  • Diagnose evaluation mapping, quality, safety, latency, token usage and tracing failures in GenAI applications.
  • Improve retrieval and customization through compatible embeddings, hybrid search, validated data and held-out evaluation.

Course Content

Practice tests

  • MLOps Infrastructure and Training Foundations
  • Model Lifecycle and Production Operations
  • Foundry Infrastructure and Prompt Management
  • GenAI Evaluation, Retrieval and Customization
  • Mixed Readiness Mock 1 — Evidence and Release Decisions
  • Mixed Readiness Mock 2 — Diagnosis and Operating Constraints