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Associate · AI-300Prep available

Microsoft Certified: Machine Learning Operations Engineer Associate (AI-300) practice exam and study path

A free, timed 60-question AI-300 practice exam in 120 minutes, scored by domain, with every option explained after you submit. Also 116 practice questions you can check one at a time, a page for every task in the exam guide, and a study path. No sign-up.

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MLOps Engineer Associate exam pack:

$6.99for 12 months

For engineers who run machine learning and generative AI in production on Azure: Azure Machine Learning workspaces, assets, pipelines, endpoints and model monitoring with MLflow, Bicep and GitHub Actions, and Microsoft Foundry projects, model deployments, evaluations, tracing, RAG tuning and fine-tuning.

The exam

Exam code
AI-300
Questions
Not yet published
Time
120 minutes
Passing score
700 of 1,000 (scaled)
Exam fee
$165 (USD)
Question types
multiple choice, multiple response

Exam AI-300 is a proctored role-based exam that replaced DP-100 in May 2026: 120 minutes, offered in English through Pearson VUE, it may include interactive components, and 700 passes. Microsoft doesn't publish the number of questions (most of its exams have 40 to 60). The fee is set by the country or region (USD 165 in the United States, the price of Microsoft's role-based exams), and the certification renews every year with a free online assessment. Our forms use 60 questions split 11 / 18 / 15 / 8 / 8, close to the midpoints of the guide's ranges.

From Microsoft's certification page and exam guide, checked Nov 1, 2026.

Domains and weights

  • Domain 1: Design and implement an MLOps infrastructure

    19%

  • Domain 2: Implement machine learning model lifecycle and operations

    30%

  • Domain 3: Design and implement a GenAIOps infrastructure

    24%

  • Domain 4: Implement generative AI quality assurance and observability

    14%

  • Domain 5: Optimize generative AI systems and model performance

    13%

On our roadmap this comes after Azure AI Fundamentals. See every certification.

Practice

116 original practice questions, every option explained with the Microsoft documentation page that proves it. Free, no sign-up; progress stays on this device.

Your results on this device →

More practice exams

The lessons, the sample questions on every task page and the first full practice exam are free and stay free. A pack adds the further timed AI-300 practice exams.

MLOps Engineer Associate exam pack

$6.99for 12 months

  • Every paid AI-300 practice exam, timed, with every option explained
  • The next version of this exam included when it changes within your 12 months
  • 12 months of access. One payment, nothing renews by itself.
  • 14-day refund, no conditions
  • 14-day refund, no conditions: ask within 14 days of buying and you get the whole payment back.
  • No pass guarantee. These are practice exams written by us from the exam guide and the Microsoft documentation; a score here does not predict your result on the real exam.

Study path

One module per exam domain. Lessons that already exist on BytePatterns are linked; the rest are being written.

  1. Module 1: Design and implement an MLOps infrastructure

    1. 1Workspaces, hubs and their associated resourcesTask 1.1

      Lesson coming

    2. 2Datastores and identity-based data accessTask 1.1

      Partly covered by: Shared Responsibility & IAM

    3. 3Compute instances, compute clusters and serverless computeTask 1.1

      Partly covered by: AWS Cost Levers

    4. 4Workspace roles, managed identities and customer-managed keysTask 1.1

      Partly covered by: Shared Responsibility & IAM, KMS, Envelope Encryption & Private CA

    5. 5Data assets and their versionsTask 1.2

      Lesson coming

    6. 6Curated and custom environments and image buildsTask 1.2

      Partly covered by: Images, Layers & Multi-Stage Builds

    7. 7Components and registries for sharing assets across workspacesTask 1.2

      Lesson coming

    8. 8GitHub Actions with OpenID Connect and the Azure Machine Learning CLITask 1.3

      Lesson coming

    9. 9Bicep templates for workspaces and resourcesTask 1.3

      Lesson coming

    10. 10Managed virtual networks, private endpoints and outbound rulesTask 1.3

      Partly covered by: VPC: Subnets, NAT & Firewalls

    11. 11Git source control for machine learning projectsTask 1.3

      Lesson coming

  2. Module 2: Implement machine learning model lifecycle and operations

    1. 12Experiment tracking and run comparison with MLflowTask 2.1

      Lesson coming

    2. 13Automated machine learning and hyperparameter sweepsTask 2.1

      Lesson coming

    3. 14Training scripts, distributed training and pipelinesTask 2.1

      Partly covered by: Training vs Inference

    4. 15Feature retrieval specifications and MLflow model registrationTask 2.2

      Lesson coming

    5. 16Responsible AI dashboard and model archivingTask 2.2

      Lesson coming

    6. 17Managed online endpoints and batch endpointsTask 2.3

      Partly covered by: Training vs Inference

    7. 18Testing and troubleshooting endpointsTask 2.3

      Partly covered by: Probes & Self-Healing

    8. 19Blue/green rollout, mirrored traffic, autoscaling and rollbackTask 2.3

      Partly covered by: Scheduling & Rolling Updates, Requests, Limits & HPA

    9. 20Data collection and model monitoring signalsTask 2.4

      Lesson coming

    10. 21Endpoint metrics, alerts and retraining triggersTask 2.4

      Partly covered by: CloudWatch, Alarms & X-Ray

  3. Module 3: Design and implement a GenAIOps infrastructure

    1. 22Foundry resources and projectsTask 3.1

      Lesson coming

    2. 23Foundry roles and managed identitiesTask 3.1

      Partly covered by: Shared Responsibility & IAM

    3. 24Foundry network isolation and private endpointsTask 3.1

      Partly covered by: VPC: Subnets, NAT & Firewalls

    4. 25Bicep and Azure CLI for Foundry resources and deploymentsTask 3.1

      Lesson coming

    5. 26Deployment types, managed compute and model selectionTask 3.2

      Lesson coming

    6. 27Model versions, upgrade policies and the model lifecycleTask 3.2

      Lesson coming

    7. 28Provisioned throughput units and spilloverTask 3.2

      Lesson coming

    8. 29Designing prompts and comparing prompt variantsTask 3.3

      Partly covered by: Evaluating LLMs

    9. 30Prompt and agent versions in GitTask 3.3

      Lesson coming

  4. Module 4: Implement generative AI quality assurance and observability

    1. 31Test datasets, data mapping and AI quality evaluatorsTask 4.1

      Partly covered by: Evaluating LLMs, LLM as a Judge

    2. 32Risk and safety evaluationsTask 4.1

      Partly covered by: Guardrails

    3. 33Automated evaluation workflowsTask 4.1

      Lesson coming

    4. 34Metrics, tokens and cost for model deploymentsTask 4.2

      Partly covered by: CloudWatch, Alarms & X-Ray

    5. 35Tracing, logging and debugging in productionTask 4.2

      Partly covered by: CloudWatch, Alarms & X-Ray

  5. Module 5: Optimize generative AI systems and model performance

    1. 36Chunk sizes, similarity and retrieval strategiesTask 5.1

      Partly covered by: Chunking and Reranking, Cosine Similarity

    2. 37Embedding models for domain-specific retrievalTask 5.1

      Partly covered by: Embeddings

    3. 38Hybrid and semantic ranking in Azure AI SearchTask 5.1

      Partly covered by: Chunking and Reranking

    4. 39RAG relevance metrics and A/B comparisonsTask 5.1

      Partly covered by: Evaluating LLMs

    5. 40Fine-tuning methods: supervised, preference and reinforcementTask 5.2

      Partly covered by: Fine-Tuning vs Prompting, Adapters and LoRA

    6. 41Training data, synthetic data and checkpointsTask 5.2

      Lesson coming

    7. 42Deploying and operating fine-tuned modelsTask 5.2

      Lesson coming

Every task in the exam guide

The exam guide splits each domain into task statements. Each one has a page with a short summary, the lessons that teach it and open sample questions.

Domain 1: Design and implement an MLOps infrastructure

Domain 2: Implement machine learning model lifecycle and operations

Domain 3: Design and implement a GenAIOps infrastructure

Domain 4: Implement generative AI quality assurance and observability

Domain 5: Optimize generative AI systems and model performance

How these questions are made

  • Written by us from the exam guide's task statements and the Microsoft documentation — never from real exam content.
  • Every option carries its own explanation and a link to the Microsoft page that proves it.
  • A question enters a timed exam only after an independent check; until then it is a practice question.
  • Results are a plain percentage, per domain too — not an imitation of Microsoft's scaled score.

Questions about the AI-300 practice exam

Is the AI-300 practice exam free?
Yes. The full practice exam (60 questions, 120 minutes) is free with no sign-up, and so are the 116 practice questions, the task pages and the study path. Your attempts are saved in this browser, on this device.
How many questions does it have, and is it timed?
60 questions in 120 minutes: the real AI-300 pace, scaled to this form. When the time runs out, the exam is submitted as it stands.
Are the answers explained?
After you submit, not during: like the real exam, the timed exam gives no feedback until the end. Then you get your score overall and per exam domain, and every question with each option explained and a link to the Microsoft documentation page behind it. In practice mode an answer is explained as soon as you check it.
What does the MLOps Engineer Associate exam pack add?
3 more timed practice exams (Practice exam 1: 60 questions, 120 minutes; Practice exam 2: 60 questions, 120 minutes; Practice exam 3: 60 questions, 120 minutes), each scored and explained the same way, and the next version of this exam if it changes within your 12 months. 12 months of access. One payment, nothing renews by itself. Everything above stays free. See the pack.
Can I get a refund?
Yes, with no conditions: ask within 14 days of buying and you get the whole payment back. The details are in the terms.
Is this an official Microsoft practice exam?
No. BytePatterns is not affiliated with, endorsed or sponsored by Microsoft. Every question is written by us from the public exam guide and the Microsoft documentation, never from real exam content, and a score here does not predict your result on the real exam. Microsoft lists its own preparation resources on its certification page.