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- 400 exam-style questions, each with a full explanation
- 20 performance-based tasks, marked with partial credit
- 200 flashcards, filtered by domain
- The full study guide, 26 chapters
- Timed mock exams matched to the real exam length
- A readiness score weighted by the official exam blueprint
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Microsoft AI-300 (Operationalizing Machine Learning and Generative AI Solutions) is the exam behind the Machine Learning Operations Engineer Associate certification: running Azure Machine Learning workspaces as provisioned infrastructure, taking a model from training job to monitored endpoint, and doing the equivalent for generative AI applications and agents in Microsoft Foundry. It targets data scientists and ML engineers who now own the pipeline, the deployment and the observability rather than only the notebook.
Practice content last updated · Independently written and aligned to Microsoft’s published exam objectives.
About the Microsoft AI-300 Exam
AI-300 is the exam Microsoft wrote for the person who keeps machine learning running in production, not for the person who trains a model once. The Azure Data Scientist Associate certification behind DP-100 is retired, and the work moved here. AI-300 treats Azure Machine Learning workspaces, datastores, compute targets and registries as infrastructure you provision with Bicep and the Azure CLI, and it expects you to automate that provisioning with GitHub Actions. The blueprint splits across five skills, and its shape catches people out. Model lifecycle and operations is the largest area at 25 to 30 percent: MLflow experiment tracking, hyperparameter sweeps, model registration, real-time and batch endpoints, progressive rollout, drift detection and retraining triggers. Add the MLOps infrastructure skill and roughly half the exam is traditional Azure Machine Learning. The rest is GenAIOps in Microsoft Foundry: foundation models on serverless API endpoints or managed compute, provisioned throughput units for high-volume workloads, prompt versioning in Git, groundedness and relevance evaluation, risk and safety checks, tracing, token cost tracking, RAG tuning and fine-tuning. Candidates from a data science background tend to lose marks on the infrastructure side rather than the modelling side. Questions ask which identity a workspace should use to reach a datastore, whether a private endpoint or a service endpoint satisfies a network restriction, and which rollout strategy lets you withdraw a bad model version without downtime. Those are DevOps decisions with an Azure Machine Learning accent, and they are half the paper.
Exam Domains Covered
- Design and implement an MLOps infrastructure19%
- Implement machine learning model lifecycle and operations30%
- Design and implement a GenAIOps infrastructure25%
- Implement generative AI quality assurance and observability13%
- Optimize generative AI systems and model performance13%
Exam Format & Details
Microsoft has not published an exact question count for AI-300; its general guidance is that most role-based exams run 40 to 60 questions. Expect multiple-choice and multiple-response items alongside the interactive types Microsoft uses on role-based exams, including drag and drop, build list, active screen and hot area, all set in AIOps scenarios rather than definition recall. The exam page states you have 120 minutes to complete the assessment, which is Microsoft's duration tier for associate exams that may contain labs, and the exam policy notes you may have interactive components. Scored on a scale of 1 to 1000 with 700 required to pass, which this app tracks as 70%. Booked through Pearson VUE, either online-proctored or at a test centre; the standard US associate-exam voucher price is 165 USD, with regional pricing elsewhere. English is the only language listed on the exam page.
Why Practice Questions Matter
AI-300 questions describe a pipeline that already exists and ask which change satisfies a stated constraint: a workspace that must reach storage with no public access, a drift signal that has to trigger retraining, an evaluation that has to catch ungrounded answers before release. Recalling service names does not get you there, because two options usually both work technically and only one meets the constraint. Practising against a bank written the same way shows you where you would guess, whether that is sizing provisioned throughput, choosing a safe rollback, or knowing which Foundry evaluator measures groundedness rather than relevance, while guessing still costs nothing.
Try 2 performance tasks free
Drag-and-drop, sequencing and configuration tasks that mirror the interactive questions on the real Microsoft AI-300 exam, marked with partial credit.
Sample Practice Questions
The following questions are a preview of the type of syllabus-aligned questions you will practise in CertCrush. They reflect the format and reasoning style of the Microsoft AI-300 exam, not actual exam content.
Q1.A compliance requirement states that no request to a Microsoft Foundry environment may arrive over the public internet. An architect must decide where the setting that switches off public access is applied. Where does publicNetworkAccess belong?
- A.On the Foundry resource, because networking is a resource-level boundary
- B.On each project, because each project has its own endpoint path
- C.On each model deployment, because the deployment receives the inference traffic
- D.On each connection, because the connection defines the route to the target service
Domain: Design and implement a GenAIOps infrastructure
Q2.Evaluation is moving from a single-turn assistant to a Foundry Agent Service agent that calls tools. What changes about the dataset shape?
- A.Flat fields give way to the messages list plus tool_definitions and tool_calls, and SDK converters can build that shape from an agent thread or run
- B.Nothing changes, because the agent evaluators read the same flat query and response fields as before
- C.The dataset must become comma-separated values, because JSONL cannot hold nested tool calls
- D.Only ground_truth is needed, because the agent evaluators simply compare the final answer with the expected answer and ignore the intermediate tool calls
Domain: Implement generative AI quality assurance and observability
Q3.After confirming that train.py works locally, a data scientist needs the platform to run it once on a CPU cluster, snapshot the code and record the logs. No hyperparameter values need to vary. Which job type should be submitted?
- A.A command job
- B.A sweep job
- C.A pipeline job
- D.An automated machine learning job
Domain: Implement machine learning model lifecycle and operations
Q4.A privacy review asks whether the prompt and answer text of a production assistant will be stored in its traces. Which statement is correct?
- A.Content recording is opt-in, enabled with AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED set to true, so prompt and answer text is not captured by default
- B.Content is recorded by default under the GenAI semantic conventions and must be switched off with a diagnostic setting on the project resource before any prompt text is stored
- C.Prompt text is always captured in the span while response text never is
- D.Content recording is governed by the continuous evaluation sampling rate setting
Domain: Implement generative AI quality assurance and observability
Q5.An operations lead runs az ml data archive --name churn-training --version 3 and the command returns no body. (Choose TWO.) Which two statements describe the outcome?
- A.Version 3 no longer appears in az ml data list output
- B.az ml data show for version 3 still returns it and jobs can still consume it
- C.The files the asset pointed at are permanently removed from the datastore
- D.Version 3 can now be re-registered with different content
- E.The empty response means the archive operation failed and must be repeated
Domain: Design and implement an MLOps infrastructure
Q6.A prompt currently at version 2.4.0 is being edited to require a new input variable, which means calling code must change. Which version number should the front matter carry after the edit?
- A.3.0.0
- B.2.5.0
- C.2.4.1
- D.2.4.0 with a note in the changelog
Domain: Design and implement a GenAIOps infrastructure
Q7.In a training pipeline, the data preparation step is cheap and CPU bound, while the training step needs a GPU. Costs must stay as low as possible. How should compute be assigned?
- A.Set default_compute to the CPU cluster in the pipeline settings and set compute on the training step to the GPU cluster
- B.Set default_compute to the GPU cluster in the pipeline settings and override compute on the prep step with the CPU cluster
- C.Submit the prep step and the training step as two separate command jobs instead of building a pipeline
- D.Set compute on the pipeline job itself so that both steps share the GPU cluster
Domain: Implement machine learning model lifecycle and operations
Q8.For one declined loan application, a reviewer must know the smallest change to the input values that would have produced an approval. Which dashboard view provides that?
- A.Counterfactual what-if
- B.Causal inference
- C.Individual feature importance
- D.Data analysis
Domain: Implement machine learning model lifecycle and operations
Q9.Answer quality dropped this week and nobody can say what the system prompt said last month, because it lives in triple quotes halfway down chat_service.py alongside unrelated code edits. Which change fixes the underlying problem?
- A.Extract the prompt into its own versioned file in the repository, separate from the code that sends it
- B.Log the full prompt string with every request so that the wording in use is always visible in the request traces
- C.Store the prompt in an application configuration setting that operations staff can edit
- D.Add a code comment above the string literal recording who last edited it and why
Domain: Design and implement a GenAIOps infrastructure
Q10.Policy states that the scoring URI must not be reachable from the public internet and must be consumed only through a private endpoint on the virtual network. Which endpoint property should you set?
- A.public_network_access: disabled
- B.auth_mode: aad_token
- C.traffic set to 0 for every deployment
- D.request_settings with a reduced request_timeout_ms
Domain: Implement machine learning model lifecycle and operations
Microsoft AI-300 guides & exam news
How to Pass the Microsoft AI-300 Exam in 2026: An 8-Week Study Plan for the New Azure Machine Learning Operations (MLOps) Engineer Certification
AI-300 is Microsoft's new MLOps Engineer Associate exam, replacing DP-100. Here is a complete 8-week study plan covering all five domains, the 700/1000 pass mark, and the Azure ML and Foundry skills you need to pass first time.
SC-500 vs SC-200 in 2026: Which Microsoft Security Exam Should You Take?
SC-500 is for engineers who build and harden Azure, hybrid and AI workloads. SC-200 is for analysts who detect, investigate and respond. How the two 2026 exams differ, where their objectives overlap, and which to take first.
Microsoft AB-650 Exam Topics and Domain Weightings in 2026: The MS-102 Replacement, Now in Beta
AB-650 replaces MS-102 as Microsoft's tenant administration exam, and the weightings tell a different story. Security and governance take 40 to 45 percent, AI services take 35 to 40 percent, and classic tenant admin is down to a fifth of the exam.
Frequently Asked Questions
Does the Microsoft AI-300 course include performance-based questions?
Yes. The Microsoft AI-300 course includes 20 performance-based questions (PBQs): hands-on tasks that mirror the interactive questions on the real exam, including drag-and-drop matching, sequencing and configuration screens. Each one is marked with partial credit, so you can see exactly which placements were wrong, and every task includes a full explanation. The first two are free to try.
What is included in the free Microsoft AI-300 sample?
The free sample includes 10 syllabus-aligned practice questions, sample flashcards, and a preview chapter from the study guide. No account or payment is required to try the sample.
How many questions are in the full Microsoft AI-300 course?
The full Microsoft AI-300 course includes 400 practice questions and 20 performance-based tasks, covering all 5 exam domains. Every question carries a full explanation for the right answer and the wrong ones.
Are these official Microsoft exam questions?
No. CertCrush questions are independently written and syllabus-aligned. They mirror the format, difficulty, and reasoning style of the official exam. We are not affiliated with or endorsed by Microsoft.
Which domains does the Microsoft AI-300 course cover?
The course covers 5 exam domains: Design and implement an MLOps infrastructure, Implement machine learning model lifecycle and operations, Design and implement a GenAIOps infrastructure, Implement generative AI quality assurance and observability, Optimize generative AI systems and model performance.
Can I study on mobile?
Yes. CertCrush is fully responsive and works on phones, tablets, and desktops. The timed exam, flashcards, and study guide all work on mobile without installing an app.
What happens when I create an account?
Creating a free account lets you access full courses, track your weak areas by domain, and resume practice sessions across devices. No credit card is required to register.
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No account, no card. The full Microsoft AI-300 course is $9.99, once.