Free AI-300 Practice Questions With Full Rationale
10 MLOps and GenAIOps scenarios split 2 / 3 / 2 / 2 / 1 across the five real exam domains, with the reasoning behind every correct answer and distractor.
TL;DR:These 10 AI-300 practice questions test operating machine learning and generative AI systems in production — MLOps infrastructure, model lifecycle, GenAIOps infrastructure, quality/observability, and optimization — weighted 2/3/2/2/1 to match the real exam's domain split. Every question includes the rationale for the correct answer and why each distractor fails.
How to Use These AI-300 Practice Questions
Answer each question before reading the rationale. AI-300 gives roughly 150 seconds per question on the real exam (100 minutes for around 40 questions), so try holding yourself to that pace — the goal is catching where your MLOps reasoning breaks down under time pressure, not just landing on the right letter eventually.
For 30 more free questions in this format, start the free AI-300 quiz. For a domain-by-domain refresher, the AI-300 cheat sheet covers every domain as a quick-reference table.
MLOps Infrastructure
Question 1
A company has one Azure Machine Learning workspace shared by two data science teams. Team A must not be able to access Team B's datastores or compute targets, even though both work in the same workspace. What should the ML engineer configure first?
- A. Split Team A and Team B into two separate workspaces
- B. Configure role-based access control (RBAC) scoped to each team's specific datastores and compute targets within the shared workspace
- C. Give both teams full Owner access and rely on a written policy not to touch each other's resources
- D. Disable the datastores entirely until access is manually requested each time
Correct: B. This matches the identity and access management objective directly — scoped RBAC on the specific resources achieves real isolation within one workspace, without the operational overhead of splitting workspaces.
- Why not A: A bigger operational change than necessary when granular access control solves the actual problem.
- Why not C: A policy is not a technical control — nothing stops either team from accessing the other's resources.
- Why not D: Breaks normal workflow entirely and is not a real access-control mechanism.
Question 2
An ML engineering team wants every new Azure Machine Learning workspace and its resources deployed consistently across dev, test, and production, with the exact configuration version-controlled and reviewable before deployment. Which approach fits?
- A. Manually create each workspace through the Azure portal in each environment, following a written checklist
- B. Deploy workspaces and resources using Bicep templates and Azure CLI, integrated with GitHub for source control and review
- C. Export one workspace's settings as a backup file and re-import it manually in each environment
- D. Ask each data scientist to configure their own workspace individually
Correct: B. This is the infrastructure-as-code objective directly: Bicep and Azure CLI deployment integrated with GitHub gives version control, review, and consistency across environments.
- Why not A: A checklist is not version-controlled and drifts easily between environments over time.
- Why not C: Not a real IaC workflow — no source control, no review step before deployment.
- Why not D: Guarantees drift across environments, the opposite of the stated goal.
Model Lifecycle
Question 3
A data science team is running dozens of training jobs with different hyperparameters and needs to compare model performance across all of them before choosing which to register. What should they configure?
- A. Configure experiment tracking with MLflow to log and compare metrics across all training jobs
- B. Manually record each run's results in a spreadsheet
- C. Only keep the results of the very last training job and discard the rest
- D. Re-run the same hyperparameters multiple times to double-check consistency instead of comparing different configurations
Correct: A. MLflow experiment tracking is built for exactly this: logging and comparing model performance across many training jobs before registration.
- Why not B: Doesn't scale across dozens of runs and ignores the built-in tracking capability designed for this.
- Why not C: Defeats the purpose of comparing across configurations — discards the data needed to make the decision.
- Why not D: Doesn't address the actual need, which is comparing different hyperparameter configurations, not re-verifying one.
Question 4
A newly deployed model version is causing a spike in incorrect predictions in production. The team needs to revert to the previous stable version with minimal downtime and minimal risk to the small percentage of users already seeing the new version.
- A. Delete the new model version immediately and redeploy the old one from scratch
- B. Use a progressive rollout and safe rollback strategy to shift traffic back to the previous stable version
- C. Leave the new version live and ask users to report errors manually before deciding
- D. Increase the compute allocated to the new version, assuming it will self-correct
Correct: B. This is exactly what progressive rollout and safe rollback strategies are for — controlled traffic shifting back to a known-good version with minimal downtime.
- Why not A: Slower and riskier than using the rollout infrastructure already meant for this exact situation.
- Why not C: Ignores available deployment safety tooling and adds risk while waiting on manual reports.
- Why not D: More compute doesn't fix a prediction-quality problem — the wrong lever entirely.
Question 5
A fraud-detection model's accuracy has quietly degraded over several months in production, even though the model itself was never changed. What should the team have had in place to catch this earlier?
- A. Automated hyperparameter tuning re-run monthly
- B. Data drift detection and performance monitoring with retraining or alert triggers configured against production data
- C. A one-time model evaluation using responsible AI principles at registration time
- D. Switching to batch endpoints instead of real-time endpoints
Correct: B. This matches production monitoring directly: detecting data drift and monitoring performance, with retraining or alerts triggered automatically when thresholds are crossed.
- Why not A: Addresses training-time tuning, not detecting drift in an already-deployed model.
- Why not C: A point-in-time check at registration — it can't catch degradation that happens months later in production.
- Why not D: An inference-serving choice unrelated to detecting drift.
GenAIOps Infrastructure
Question 6
A financial services company deploying Microsoft Foundry resources must ensure that only specific service identities — not individual user passwords — can access Foundry resources, and that traffic never traverses the public internet. Which two capabilities should be configured?
- A. Managed identities with role-based access control (RBAC), plus private networking configurations
- B. Shared service account passwords rotated monthly, plus a public endpoint with IP allowlisting
- C. A single global admin account shared across the team, plus a VPN client on each laptop
- D. API keys embedded directly in application code, plus disabling logging to reduce exposure
Correct: A. This matches the objective directly: managed identities with RBAC replace password-based access, and private networking configurations keep traffic off the public internet.
- Why not B: Still password-based (violates the no-password requirement) and a public endpoint contradicts "never traverses the public internet."
- Why not C: A shared admin account is not a managed identity, and a client VPN is not the same as private networking on the resource itself.
- Why not D: Hardcoded API keys are not managed identities, and disabling logging reduces visibility rather than improving security.
Question 7
A generative AI application built on a Foundry-deployed foundation model experiences unpredictable response times during peak hours, even though the model itself hasn't changed. Business stakeholders need consistent, predictable latency for a fixed, known volume of daily requests. Which deployment approach addresses this?
- A. Switch to a serverless API endpoint with default settings
- B. Configure provisioned throughput units sized to the known request volume
- C. Reduce the model's context window to speed up every request
- D. Deploy the same model redundantly in five different regions with no traffic management
Correct: B. Provisioned throughput units are the mechanism built for predictable performance at a known, fixed volume — exactly what the scenario asks for.
- Why not A: Serverless/consumption-based endpoints suit variable, unpredictable load, not a guarantee of consistent latency at known volume.
- Why not C: A blunt change unrelated to the latency-predictability problem described.
- Why not D: Adds cost and complexity without provisioning capacity or managing traffic, so it doesn't guarantee latency either.
Quality & Observability
Question 8
An evaluation run shows a generative AI assistant's answers are topically on-target and well-written, but frequently include details that aren't actually supported by the source documents it was given. Which quality metric is failing?
- A. Fluency — how grammatically natural the text reads
- B. Coherence — how logically consistent the response is internally
- C. Groundedness — whether the response is factually supported by the provided source content
- D. Relevance — how on-topic the response is
Correct: C. Groundedness measures exactly this — whether a response is factually supported by the source material — and the scenario describes unsupported details, the classic groundedness failure.
- Why not A: The scenario says the writing itself is fine — fluency is not the failing dimension.
- Why not B: The issue isn't internal logical consistency, it's factual grounding — a different dimension.
- Why not D: The scenario explicitly says the response is topically on-target, so relevance is not what's failing.
Question 9
Cost for a generative AI application has grown significantly month over month with no corresponding increase in user traffic. Leadership wants to know exactly where the spend is going before approving further budget. What should the team configure to answer this?
- A. Reduce the number of users who can access the application
- B. Track cost metrics, including token consumption and resource usage, alongside detailed logging and tracing for production troubleshooting
- C. Switch to a cheaper foundation model without first investigating the cause
- D. Disable monitoring to reduce overhead and cost
Correct: B. This matches the observability objective directly: cost metrics like token consumption plus logging/tracing are exactly what leadership needs to see where spend is actually going.
- Why not A: Masks the symptom without diagnosing the actual cause.
- Why not C: Premature — could reduce quality without fixing the real cost driver, and skips diagnosis entirely.
- Why not D: Removes the exact visibility needed to answer the question being asked.
Optimization
Question 10
A team has fine-tuned a foundation model on proprietary support-ticket data. Before promoting it from a development environment to production, what should they do to ensure the fine-tuned model is properly managed through its lifecycle?
- A. Deploy it directly to production immediately since fine-tuning already improved its accuracy in testing
- B. Monitor and optimize the fine-tuned model's performance, and manage it through a defined path from development to production deployment
- C. Retrain from scratch using only synthetic data instead of the proprietary support-ticket data
- D. Archive the base foundation model since a fine-tuned version now exists
Correct: B. This matches the fine-tuning lifecycle objective directly: monitoring/optimizing performance and managing the model through a defined dev-to-production path, not a one-time promotion.
- Why not A: Skips the lifecycle management and monitoring step the objective specifically calls for.
- Why not C: Discards the actual fine-tuning work and the real-data advantage — not what the scenario asks for.
- Why not D: An unrelated action that doesn't address managing the fine-tuned model's own lifecycle.
Distractor Patterns to Watch For on AI-300
Across these 10 questions, the wrong answers cluster into a handful of repeatable traps:
| Pattern | What it looks like |
|---|---|
| Skips the built-in tooling | Doing something manually (spreadsheets, checklists, individual configs) instead of using the platform capability built for exactly that job — MLflow tracking, IaC, RBAC. |
| Fixes the wrong layer | Throwing more compute or a model change at a problem that's actually about deployment strategy, cost tracking, or access control. |
| Confusing similar-sounding quality metrics | Groundedness vs. relevance vs. coherence vs. fluency each test a different dimension of the same vague "something feels off" symptom. |
| Point-in-time check mistaken for monitoring | A one-time evaluation or test treated as equivalent to the continuous drift, cost, or performance monitoring the scenario actually needs. |
| Bigger hammer than necessary | Splitting workspaces, redeploying from scratch, or disabling access wholesale when a targeted, built-in control solves it directly. |
MSCertQuiz sells practice-exam access for AI-300 and other Microsoft certifications; these 10 questions are a free sample from the same 500-question bank the team maintains.
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