AI-300: Operationalizing Machine Learning and Generative AI Solutions Practice Exam

What the AI-300 exam covers

• Design and implement an MLOps infrastructure142 questions
• Implement machine learning model lifecycle and operations218 questions
• Design and implement a GenAIOps infrastructure177 questions
• Implement generative AI quality assurance and observability129 questions
• Optimize generative AI systems and model performance127 questions

Free AI-300 sample questions

A sample of 10 questions with answers and explanations. Sign up free to practice all 793.

1. Question 1Design and implement an MLOps infrastructure

   You are creating an Azure Machine Learning workspace for a team that must keep training data, model artifacts, and metadata inside a single subscription. Which set of Azure resources does the workspace automatically provision as its dependencies?

   • AA storage account, a key vault, and Application InsightsCorrect
   • BA SQL managed instance, an event hub, and a service bus namespace
   • CAn Azure Kubernetes Service cluster and a container registry only
   • DA Log Analytics workspace and an Azure Data Factory instance
   ✓ Correct answer: A

   Creating a workspace creates a small set of associated resources it cannot function without. The storage account holds the default datastores, uploaded data, notebooks and job outputs. The key vault stores connection secrets and credentials that datastores and connections reference, so nothing sensitive lives in the workspace object itself. Application Insights receives telemetry from deployed endpoints, which is what makes inference monitoring available without extra wiring. A container registry is also associated, but it is created lazily - the first time an environment image has to be built - so a brand new workspace may not have one yet. You can bring your own instances of any of these at creation time, which is the usual choice when policy dictates specific encryption or network settings.

   Why the other options are wrong

   • BA SQL managed instance, an event hub and a service bus namespace are not workspace dependencies; none of them holds workspace data, secrets or telemetry.
   • CAn AKS cluster is an optional inference target you attach yourself, and the container registry is created on demand rather than up front.
   • DA Log Analytics workspace backs Application Insights indirectly, but neither it nor Data Factory is provisioned as a direct workspace dependency.
2. Question 2Implement machine learning model lifecycle and operationsSelect all that apply

   Which TWO practices reduce the risk that a model performing well in evaluation behaves badly in production? (Choose TWO)

   • AEvaluate on a held-out set drawn from the same period and population as production trafficCorrect
   • BCompute the serving features with the same definitions used during trainingCorrect
   • CTrain on the entire dataset including the evaluation rows to maximize accuracy
   • DReport only aggregate accuracy so results are simpler to communicate
   ✓ Correct answer: A, B

   Two classic failures are being closed here. An evaluation set that is not representative - drawn from a different period, or from a filtered population - produces a number that flatters the model and predicts nothing about live behaviour. Training and serving skew is the other: when the feature pipeline at inference computes something subtly different from what training used, the model receives inputs it never saw, and the resulting degradation looks mysterious because both halves are individually correct.

   Why the other options are wrong

   • CTraining on the evaluation rows leaks them into the model, so the reported score measures memorization rather than generalization.
   • DReporting only an aggregate hides cohort-level failure, which is one of the main ways a well-scoring model disappoints in production.
3. Question 3Implement generative AI quality assurance and observability

   What should an evaluation threshold in a release gate be based on?

   • AAn agreed quality bar for the use case, set before the results are seenCorrect
   • BWhatever score the current candidate happens to achieve
   • CThe highest score any previous model version has ever achieved
   • DA round number chosen because it is easy to remember
   ✓ Correct answer: A

   A threshold decided in advance is a decision rule; one decided after seeing the score is a rationalization. Setting it beforehand, from what the use case actually requires and what the incumbent achieves, is what makes the gate meaningful under pressure - when a release is due and the score is marginal, the argument has already been had. Tying the bar to the consequences of being wrong is what keeps a medical or financial application held to a higher standard than a draft-suggestion feature.

   Why the other options are wrong

   • BA threshold set to whatever the candidate scored passes every candidate by construction, which is a gate in appearance only.
   • CThe best score ever achieved may be unattainable or may have come from a favourable dataset, making it an arbitrary bar.
   • DMemorability is not a quality criterion; a round number bears no relationship to what the use case requires.
4. Question 4Design and implement a GenAIOps infrastructure

   What does reciprocal rank fusion do in a hybrid search pipeline?

   • AIt merges rankings from several retrieval methods into one combined orderingCorrect
   • BIt removes duplicate documents from the corpus before the index is built for the first time
   • CIt rewrites the user's query into several variants and issues each of them separately
   • DIt compresses retrieved documents so that more of them fit into the context window
   ✓ Correct answer: A

   Vector search and keyword search produce scores on incompatible scales, so they cannot simply be added. Reciprocal rank fusion sidesteps that by using each document's rank rather than its score, summing a reciprocal of the position across the result lists. A document ranked well by either method surfaces, and one ranked well by both surfaces higher, which is exactly the behaviour hybrid retrieval wants without any score normalization.

   Why the other options are wrong

   • BRemoving duplicate documents is an indexing concern handled during ingestion, unrelated to combining result rankings.
   • CIssuing query variants is query expansion, a different technique that happens before retrieval rather than after.
   • DCompressing retrieved documents shortens their text; fusion changes the order of results without altering their content.
5. Question 5Design and implement an MLOps infrastructure

   Which practice makes it possible to recreate an entire Azure Machine Learning environment after an accidental deletion?

   • AKeep the infrastructure definitions and asset registrations in source controlCorrect
   • BRely on the platform's soft delete to restore everything that was removed
   • CTake a monthly screenshot of the workspace's configuration pages in the portal
   • DAsk each team to remember the settings they configured for their own resources
   ✓ Correct answer: A

   Recreation is only possible from a definition, so the workspace, its dependencies, its compute, its environments and its asset registrations all belong in the repository as code. Soft delete helps for the workspace resource within its retention window, but it is a safety net with a time limit rather than a recovery plan, and it does not cover everything. A repository that can rebuild the environment from scratch is what makes the answer to deletion straightforward.

   Why the other options are wrong

   • BSoft delete covers the workspace resource for a limited window and is not a general recovery mechanism.
   • CA monthly screenshot of the configuration pages is unstructured, quickly outdated, and cannot be applied to recreate anything.
   • DAsking each team to remember the settings they configured produces an approximation at best and fails when people are unavailable.
6. Question 6Implement machine learning model lifecycle and operations

   A pipeline's evaluation step compares a candidate against the incumbent. Where should the incumbent's model version come from?

   • AThe registry, resolved by the tag identifying what is currently deployedCorrect
   • BA copy of the incumbent stored in the pipeline's own source repository
   • CWhichever model version was registered immediately before the candidate
   • DThe version an engineer names manually when starting the pipeline run
   ✓ Correct answer: A

   The comparison is only meaningful against what is actually serving traffic, so the incumbent has to be resolved from the source of truth rather than assumed. A tag identifying the deployed version makes that a query, and it stays correct after a rollback - which is exactly when a naive assumption breaks, because the previously registered version is no longer the one in production.

   Why the other options are wrong

   • BA copy in the repository bloats it and goes stale the moment production changes to a different version.
   • CThe most recently registered version is not necessarily deployed, particularly after a rollback.
   • DA manually named version depends on someone knowing what is deployed and reintroduces human error.
7. Question 7Optimize generative AI systems and model performance

   Which is the most reliable way to reduce hallucination in a generative feature?

   • AGround answers in retrieved context and instruct the model to decline when it is insufficientCorrect
   • BIncrease the model's temperature so it explores more possible answers before responding
   • CAsk the model to state its confidence and discard answers below a threshold
   • DUse a larger model, since larger models do not fabricate information
   ✓ Correct answer: A

   Hallucination is most effectively addressed by giving the model the material and constraining it to that material. The second half matters as much as the first: a model told to answer from context but not told what to do when the context is inadequate will fill the gap. An explicit instruction to say the sources do not cover it, plus groundedness measurement, is what makes the constraint real.

   Why the other options are wrong

   • BHigher temperature increases variability, which makes fabrication more likely rather than less.
   • CSelf-reported confidence is poorly calibrated, so a threshold on it filters unreliably in both directions.
   • DLarger models fabricate less in some cases but still fabricate; size is not a solution to grounding.
8. Question 8Optimize generative AI systems and model performance

   Which describes an appropriate use of a small model as a pre-filter in front of a larger one?

   • AClassify or route requests cheaply so only the hard ones reach the expensive modelCorrect
   • BGenerate a draft that the large model then rewrites for every single request
   • CScore the large model's output and regenerate it whenever the score is low
   • DDuplicate every request to both models and compare their answers each time
   ✓ Correct answer: A

   A small model performing classification or routing costs a fraction of a full generation, so using it to decide which requests genuinely need the expensive model captures most of the saving with no quality loss on the easy majority. The design work is the routing rule and a fallback for when the small model's judgement is wrong. The other patterns all call the expensive model anyway, so they add cost rather than removing it.

   Why the other options are wrong

   • BDrafting then rewriting calls both models on every request, which costs more than calling the large one alone.
   • CScoring the large model's output and regenerating when the score is low adds calls on top of the expensive generation.
   • DDuplicating to both models pays for both on every request with no saving whatsoever.
9. Question 9Implement generative AI quality assurance and observability

   Which describes the correct handling of an agent that produced a harmful response in production?

   • AContain it, capture the full trace, and add the case to the safety evaluation setCorrect
   • BDelete the conversation record so the harmful content is not retained
   • CAdjust the prompt immediately without recording what happened
   • DWait to see whether the behaviour recurs before taking any action
   ✓ Correct answer: A

   Containment stops the harm, the trace is what makes diagnosis possible, and adding the case to the safety set is what stops the same failure recurring silently after a future change. That last step is the one most often skipped, and it is what turns an incident into a permanent improvement. Deleting the record removes the evidence needed for both the fix and any reporting obligation.

   Why the other options are wrong

   • BDeleting the record destroys the evidence needed to diagnose the failure and to meet reporting obligations.
   • CChanging the prompt without recording the case means nothing verifies the fix or prevents a recurrence.
   • DWaiting for recurrence leaves the harmful behaviour reachable by users in the meantime.
10. Question 10Design and implement an MLOps infrastructure

    Which is the appropriate way to grant a partner organization limited access to a machine learning workspace?

    • AInvite specific external identities and assign narrowly scoped roles with a review dateCorrect
    • BCreate shared local accounts for the partner's team to use
    • CGrant access to the partner's entire tenant at subscription scope
    • DShare an existing team member's credentials for the duration of the work
    ✓ Correct answer: A

    External identity invitation keeps each partner person identifiable, which is what makes their activity attributable and their access individually revocable when they leave the engagement. Narrow scoping limits what they can reach, and a review date is what prevents the access outliving the project - which is how external access accumulates unnoticed.

    Why the other options are wrong

    • BShared accounts destroy attribution, so nothing done by the partner can be traced to a person.
    • CTenant-wide access at subscription scope grants far more than the engagement requires.
    • DSharing credentials is both unattributable and a breach of most acceptable use policies.

Who this AI-300 practice exam is for

This practice set is for anyone preparing for the AI-300: Operationalizing Machine Learning and Generative AI Solutions exam - from first-time candidates building a foundation to experienced Microsoft practitioners doing a final review before test day. If you learn best by working through realistic questions and reading why each answer is right or wrong, it is built for you.

How to use this AI-300 practice exam

1. Start with the free sample questions above to gauge your current baseline.
2. Read the full explanation on every question, including why each wrong option is wrong.
3. Track your weak domains and focus your study where you are losing the most marks.
4. Once you are scoring consistently well, take a timed, full-length mock exam.
5. Use your readiness score to decide when you are ready to book the real AI-300 exam.

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