Microsoft AI-300: MLOps & GenAIOps Practice Tests

This course contains the use of artificial intelligence.

Getting a model to run is different from keeping an AI application reliable in production. A training job can finish without preserving the right artifact. A fluent answer can be unsupported. A private endpoint can work while a connected data source remains inaccessible. These practice questions help you reason through those boundaries before selecting a configuration or approving a release.

Prepare for AI-300: Operationalizing Machine Learning and Generative AI Solutions, the exam for Microsoft Certified: Machine Learning Operations Engineer Associate. The course follows the five-domain Microsoft study guide checked on October 13, 2026, spanning both Azure Machine Learning operations and Microsoft Foundry generative AI operations.

What is included

• 324 original questions across six tests, with single-answer and explicitly counted multiple-answer items.

• Four focused sets of 51 questions: MLOps infrastructure and training foundations; model lifecycle and production operations; Foundry infrastructure and prompt management; and GenAI evaluation, observability, retrieval and customization.

• Two mixed readiness mocks of 60 questions and 120 minutes each, with domain proportions inside the published AI-300 ranges.

• An explanation for every answer option, a condition-based overall explanation and a focused study suggestion for every question.

• Practical diagnosis, resource and identity decisions, release gates, model and prompt versioning, metric interpretation and small Python reasoning exercises.

Train the decisions behind the tools

Work through credentialless data access, compute and environment choices, reusable pipeline components, registries, GitHub federation and declarative infrastructure. Then follow a model from experimentation through feature retrieval, registration, deployment, safe rollout, monitoring and retraining decisions.

For generative AI, compare supported serving options and capacity needs, control prompt releases, map evaluation data correctly, distinguish quality from safety and authorization, and trace performance and cost across the request path. Retrieval questions cover evidence boundaries, embedding compatibility, hybrid ranking and experimental design. Customization questions connect training signals to supported methods, synthetic-data quality and controlled serving of tuned artifacts.

A useful study sequence

1. Complete the focused sets in manageable sessions. Explain why each competing option fails under the stated conditions.

2. Review the feedback and study suggestion, then revisit the corresponding Microsoft Learn material or a permitted hands-on environment.

3. Take a mixed mock without checking explanations. Use its domain results to identify remaining gaps.

4. Reattempt uncertain decisions after studying the underlying rule, rather than memorizing answer positions.

The two mocks are author-set written readiness exercises. Microsoft currently lists 120 minutes for AI-300 and a passing score of 700 on its scaled scoring system; it does not publish a fixed AI-300 question count or exact item-type allocation. Our 60-question forms and 80% practice target do not reproduce that scoring system or the interactive exam components. The focused sets allow 110 minutes each and can be studied in shorter sessions.

This is an independent practice course, not a Microsoft product or endorsement. It contains original learning questions rather than recalled or confidential exam items. It provides practice and explanations, not a hosted Azure lab, and does not guarantee an exam result. Model availability, deployment support and preview status can change; questions state relevant constraints and explain where compatibility must be checked.