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AI-300 · Sample questions
6 real questions from the CertifyDeck AI-300 bank, spread across the domains of the official Microsoft Certified: Machine Learning Operations Engineer Associate skills outline, with the correct answer and a full explanation of why the other options are wrong. They are original questions written from the public syllabus, not exam dumps, and they are the same style you will find in the practice tests and mock exams.
Correct answer: A. Create or update the Azure Machine Learning workspace
Create or update the Azure Machine Learning workspace is appropriate because its primary purpose is to establish the central resource for managing machine learning assets, jobs, compute, and endpoints. Configure an Azure Machine Learning datastore is not the best choice because its primary purpose is to reference an existing storage location without embedding credentials in training code. Select and configure the appropriate compute target is not the best choice because its primary purpose is to run the workload on compute whose scaling and execution model match the job. Assign a managed identity the minimum required Azure role is not the best choice because its primary purpose is to let the workspace or compute access a protected resource without stored secrets.
Correct answer: B. Evaluate candidate models against task quality, safety, latency, and cost
Configure provisioned throughput is not the best choice because its primary purpose is to provide predictable reserved model capacity for a sustained high-volume workload. Evaluate candidate models against task quality, safety, latency, and cost is appropriate because its primary purpose is to select the foundation model that best meets the workload's measured requirements. Choose a serverless API or managed-compute deployment according to the model requirements is not the best choice because its primary purpose is to consume a supported model without dedicated compute or host one that requires customizable inference compute. Deploy a versioned model candidate and use a controlled production rollout is not the best choice because its primary purpose is to upgrade a foundation model while detecting regressions and preserving rollback.
Correct answer: B. Automate built-in and custom evaluations in the delivery workflow
Measure groundedness, relevance, coherence, and fluency is not the best choice because its primary purpose is to separate factual support and task usefulness from writing quality. Automate built-in and custom evaluations in the delivery workflow is appropriate because its primary purpose is to prevent a prompt, model, or application version from shipping when quality gates fail. Run risk and safety evaluators is not the best choice because its primary purpose is to detect harmful content and safety regressions before production release. Create a representative test dataset with explicit field mapping is not the best choice because its primary purpose is to evaluate the application consistently against inputs, context, and expected evidence.
Correct answer: C. Create a representative test dataset with explicit field mapping
Measure groundedness, relevance, coherence, and fluency is not the best choice because its primary purpose is to separate factual support and task usefulness from writing quality. Run risk and safety evaluators is not the best choice because its primary purpose is to detect harmful content and safety regressions before production release. Create a representative test dataset with explicit field mapping is appropriate because its primary purpose is to evaluate the application consistently against inputs, context, and expected evidence. Automate built-in and custom evaluations in the delivery workflow is not the best choice because its primary purpose is to prevent a prompt, model, or application version from shipping when quality gates fail.
Correct answer: C. Evaluate the fine-tuned model against the baseline
Version, deploy, monitor, and roll back the fine-tuned model is not the best choice because its primary purpose is to manage the customized model safely throughout its production lifecycle. Choose the advanced fine-tuning method that matches the adaptation goal is not the best choice because its primary purpose is to customize model behavior when prompting and retrieval alone do not meet the requirement. Evaluate the fine-tuned model against the baseline is appropriate because its primary purpose is to detect quality, safety, latency, or cost regressions caused by customization. Generate, filter, and validate synthetic training data is not the best choice because its primary purpose is to expand scarce examples without silently introducing low-quality or unsafe labels.
Correct answer: A. Archive the superseded model version
Archive the superseded model version is appropriate because its primary purpose is to remove an obsolete candidate from normal selection while retaining its history. Package the feature retrieval specification with the model is not the best choice because its primary purpose is to preserve how online inference retrieves the features expected by the model. Register the MLflow model as a new version is not the best choice because its primary purpose is to manage a deployable model artifact with signature, dependencies, and lineage. Run a Responsible AI assessment is not the best choice because its primary purpose is to evaluate model errors, fairness, interpretability, and potentially harmful outcomes before release.
Answer each question before opening the explanation, and write down why you discarded the other options. On AI-300, most mistakes come from choosing an option that is technically possible but does not meet every requirement in the scenario.
These 6 questions are a taste of the format, not a diagnostic. To know which domains you fail, practice by domain for free and take a timed mock exam that mirrors the official weights and duration.
Start for free, no sign-up or card. Every question includes an explanation like the ones above, and the mock exam reproduces the official AI-300 format.