Microsoft · AI-300
Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) Practice Tests
Validates expertise in setting up infrastructure for MLOps and GenAIOps solutions on Azure, including training, deploying, and maintaining traditional ML models with Azure Machine Learning and operationalizing generative AI applications using Microsoft Foundry.
Exam Details
Practice Questions
583
≈ 11 practice exams
Duration
120 minutes
Passing Score
700/1000
Difficulty
AssociateLast Updated
May 2026
Topics Covered
Design and Implement an MLOps InfrastructureImplement Machine Learning Model Lifecycle and OperationsDesign and Implement a GenAIOps InfrastructureImplement Generative AI Quality Assurance and ObservabilityOptimize Generative AI Systems and Model Performance
Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) Practice Exam Preparation
Use this AI-300 practice exam to prepare for Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (AI-300) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 583 questions for Microsoft AI-300, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as 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, and Optimize Generative AI Systems and Model Performance. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Design and implement an MLOps infrastructure18%
Implement machine learning model lifecycle and operations28%
Design and implement a GenAIOps infrastructure23%
Implement generative AI quality assurance and observability13%
Optimize generative AI systems and model performance13%
Exam Overview
The Microsoft Certified: Machine Learning Operations Engineer Associate certification, earned by passing Exam AI-300: Operationalizing Machine Learning and Generative AI Solutions, validates expertise in designing and operationalizing both traditional machine learning and generative AI solutions on Azure. The credential covers the full AI operations (AIOps) spectrum — from provisioning infrastructure using Azure Machine Learning and Microsoft Foundry to implementing CI/CD pipelines with GitHub Actions and Infrastructure as Code (IaC) using Bicep and Azure CLI. It directly replaces the retiring Microsoft Certified: Azure Data Scientist Associate (DP-100) as of June 2026, reflecting a deliberate evolution in the Azure certification roadmap from experimental data science toward production-grade, enterprise-scale AI operations.
The exam assesses five skill domains: MLOps infrastructure design, machine learning model lifecycle management, GenAIOps infrastructure, generative AI quality assurance and observability, and optimization of generative AI systems. It covers tooling and practices such as MLflow experiment tracking, automated machine learning, real-time and batch endpoint deployment, data drift detection, RAG pipeline optimization, prompt versioning, responsible AI evaluation, and fine-tuning with synthetic data — making it one of Microsoft's most technically comprehensive associate-level certifications.
Who Should Take This Exam
This certification is designed for ML engineers, AI engineers, and cloud engineers who work at the intersection of data science, DevOps, and generative AI. Ideal candidates already have hands-on experience training, deploying, and maintaining machine learning models using Azure Machine Learning, as well as practical exposure to deploying and monitoring generative AI applications and agents through Microsoft Foundry. They collaborate with data scientists, DevOps teams, and organizational stakeholders to deliver scalable, automated AI solutions in production.
Professionals transitioning from Azure Data Scientist Associate (DP-100) will find this certification a natural progression, as it extends model training and evaluation knowledge into full lifecycle operations. It is also well-suited for DevOps engineers expanding into AI workloads and for MLOps or GenAIOps practitioners seeking formal validation of their Azure-specific skills.
Prerequisites
There are no formal prerequisites to register for Exam AI-300, but Microsoft recommends a data science background with active Python programming experience. Candidates should have an entry-level understanding of DevOps practices, particularly working with GitHub Actions and command-line interfaces (CLIs). Familiarity with Azure Machine Learning and Microsoft Foundry is expected, as the exam directly tests their use in training, deploying, and monitoring both traditional ML models and generative AI applications.
Practical experience with Infrastructure as Code using Bicep and Azure CLI is also strongly recommended. Candidates without prior exposure to concepts such as MLflow experiment tracking, managed inference endpoints, retrieval-augmented generation (RAG), and model evaluation frameworks like groundedness and relevance metrics should study those topics specifically before attempting the exam.
Exam Format
Exam AI-300 is delivered in English through Pearson VUE and is available as an online proctored or in-person exam. Candidates have 120 minutes to complete the assessment. The passing score is 700 out of 1000 on Microsoft's scaled scoring system. Question types can include multiple choice, drag-and-drop, case studies, hot area, active screen, and build list formats — consistent with Microsoft's associate-level exam experience. An exam sandbox is available on Microsoft Learn to familiarize candidates with the interface before test day.
The certification requires passing only this single exam. Like all Microsoft Associate and Expert certifications, it expires annually and can be renewed at no cost by passing a free online renewal assessment on Microsoft Learn, typically available 6 months before expiration. The exam launched in beta in early 2026 and reached general availability in May 2026.
Skills Measured
- 1.Design and implement an MLOps infrastructure (15–20%): Covers creating and managing Azure Machine Learning workspaces, datastores, compute targets, data assets, environments, and reusable components. Includes implementing IaC with Bicep and Azure CLI, automating resource provisioning via GitHub Actions workflows, configuring identity and access management (IAM), restricting network access, and managing source control with Git.
- 2.Implement machine learning model lifecycle and operations (25–30%): The heaviest domain. Covers MLflow experiment tracking, automated ML, hyperparameter tuning, distributed training, and training pipelines. Also includes model registration and versioning, responsible AI evaluation, deploying models as real-time or batch endpoints, progressive rollout and safe rollback strategies, data drift detection, production monitoring, and automated retraining triggers.
- 3.Design and implement a GenAIOps infrastructure (20–25%): Covers creating and configuring Microsoft Foundry project environments, IAM with managed identities and RBAC, private networking, Bicep-based infrastructure deployment, deploying foundation models via serverless API endpoints and managed compute, model versioning and production deployment strategies, provisioned throughput unit configuration, and prompt versioning with Git.
- 4.Implement generative AI quality assurance and observability (10–15%): Covers building evaluation datasets and data mappings, implementing AI quality metrics (groundedness, relevance, coherence, fluency), configuring risk and safety evaluations for harmful content detection, setting up automated evaluation workflows with built-in and custom metrics, monitoring latency/throughput/response times in Foundry, tracking token consumption and resource cost, and configuring logging and tracing for production troubleshooting.
- 5.Optimize generative AI systems and model performance (10–15%): Covers RAG pipeline optimization including similarity thresholds, chunk sizes, and retrieval strategy tuning; selecting and fine-tuning embedding models for domain-specific use cases; implementing hybrid search combining semantic and keyword retrieval; evaluating RAG systems with relevance metrics and A/B testing; and implementing advanced fine-tuning methods including synthetic data creation, fine-tuned model monitoring, and end-to-end production deployment of customized models.
Study Tips
- Start with the official AI-300 Study Guide on Microsoft Learn (aka.ms/AI300-StudyGuide), which maps every exam domain to specific subtopics with percentage weights. Use it as a checklist and prioritize the heaviest domain — 'Implement machine learning model lifecycle and operations' at 25–30% — first.
- Use the Microsoft Learn exam sandbox (aka.ms/examdemo) to familiarize yourself with the exact user interface, question types, and navigation before test day. This is especially useful for interactive question formats like hot area, active screen, and build list.
- Build hands-on labs in Azure Machine Learning and Microsoft Foundry. Specifically practice: creating ML pipelines with MLflow tracking, deploying a model as a real-time managed endpoint, configuring data drift monitors, and setting up a Foundry project with a deployed foundation model. The exam tests applied knowledge, not just theory.
- Study IaC and GitHub Actions integration by deploying a Machine Learning workspace using a Bicep template and automating it via a GitHub Actions workflow. The exam covers this in the MLOps infrastructure domain and many candidates underestimate how specifically it is tested.
- For the GenAIOps domains, work through Microsoft Learn modules on Microsoft Foundry specifically — including configuring managed identities, evaluating generative AI outputs using built-in quality metrics (groundedness, coherence, relevance), and setting up observability dashboards. The Exam Readiness Zone on Microsoft Learn shows (learn.microsoft.com/en-us/shows/exam-readiness-zone/) is useful supplemental video content.
- Practice RAG pipeline construction and optimization, as the 'Optimize generative AI systems' domain explicitly tests chunking strategies, hybrid search, embedding model selection, and A/B testing. Use the Azure AI Foundry documentation and the generative AI playbook linked in the study guide.
- Microsoft certifications expire annually. Plan to renew via the free online assessment on Microsoft Learn, typically available 6 months before expiration — schedule a reminder so your credential stays active.
Career Benefits
The AI-300 certification positions holders for roles such as ML Engineer, AI Operations Engineer, GenAIOps Specialist, and Cloud AI Engineer — roles that sit at a high-demand intersection of machine learning, cloud infrastructure, and generative AI. As of 2026, Azure AI Engineers in the US earn a median annual salary of approximately $111,000–$148,000, with senior and specialized practitioners reaching $190,000 or more. MLOps-specific skills, particularly around generative AI operationalization and RAG pipeline optimization, command meaningful salary premiums above general cloud engineering roles.
This certification carries additional strategic weight because it directly replaces the retiring DP-100 (Azure Data Scientist Associate), signaling that Microsoft now considers AI operationalization — not just model building — the core competency for AI professionals on Azure. Compared to the AI-102 (Azure AI Engineer Associate), which focuses on consuming Azure AI services, AI-300 is more infrastructure- and lifecycle-oriented, making it a stronger differentiator for engineers responsible for production AI systems. It is part of Microsoft's 2026 overhaul of its AI certification roadmap, aligning credentials with enterprise generative AI adoption and making AIOps fluency a baseline expectation for Azure data and AI roles.
Sample Questions
5 sample questions with answers and explanations. The full bank has 583 questions, enough for 11 full-length practice exams.
Preview — answers shown1. Fabrikam, Inc.'s data science team builds models using multiple frameworks and wants to enable unified experiment tracking by calling mlflow.autolog() at the start of every training script. Which two frameworks require the team to implement manual MLflow logging instead of relying on autolog? (Select two!)
Multiple correct answersAXGBoost
BCatBoost
CPyTorch Lightning
DVanilla PyTorch
ELightGBM
Explanation
CatBoost and vanilla PyTorch are not supported by mlflow.autolog() and require manual logging using MLflow APIs such as mlflow.log_metric() and mlflow.log_artifact(). MLflow autolog natively supports XGBoost, LightGBM, PyTorch Lightning, Scikit-learn, Keras/TensorFlow, PySpark, Spark, Statsmodels, and PaddlePaddle. PyTorch Lightning is supported because it exposes a structured callback and hook system that MLflow can instrument automatically. Vanilla PyTorch lacks this structured callback mechanism and requires explicit logging calls placed throughout the training loop. CatBoost, despite being a widely used gradient boosting framework, does not have native MLflow autolog integration and similarly requires full manual instrumentation.
2. Tailspin Toys' data science team is migrating several training workloads to Azure Machine Learning and wants to use MLflow autologging to capture metrics and parameters with minimal code changes. For which two training frameworks must they implement manual MLflow metric logging instead of relying on autologging? (Select two!)
Multiple correct answersAPyTorch Lightning
BCatBoost
CLightGBM
DProphet
EKeras
Explanation
CatBoost and Prophet are not supported by MLflow autologging in Azure Machine Learning and require developers to manually call mlflow.log_metric() and mlflow.log_param() in their training scripts. The frameworks supported by MLflow autolog include Scikit-learn, XGBoost, LightGBM, Keras/TensorFlow, PySpark, Spark, Statsmodels, PaddlePaddle, and PyTorch Lightning. Vanilla PyTorch and Fastai also require manual logging. PyTorch Lightning is specifically included in the autolog-supported list, distinguishing it from vanilla PyTorch. LightGBM and Keras are both supported by autolog and require no additional instrumentation.
3. Alpine Ski House's data science team is defining experiment tracking standards for Azure Machine Learning projects. They need to identify which ML frameworks require developers to write explicit MLflow logging calls because they are not supported by mlflow.autolog(). Which two frameworks require manual MLflow logging? (Select two!)
Multiple correct answersAScikit-learn
BCatBoost
CXGBoost
DProphet
EPyTorch Lightning
Explanation
CatBoost and Prophet do not support MLflow autologging and require developers to manually call mlflow.log_metric(), mlflow.log_param(), and mlflow.log_model() to capture experiment data. Scikit-learn, XGBoost, and PyTorch Lightning are all supported by MLflow autologging — enabling mlflow.autolog() before training automatically captures metrics, parameters, and model artifacts for these frameworks without additional instrumentation code.
4. Lamna Healthcare's MLOps team configures Azure Machine Learning model monitoring for a deployed patient risk scoring model. They need to set the production data lookback window to analyze the most recent 14 days of inference data. The monitoring configuration requires an ISO 8601 duration string. Which value correctly represents this 14-day lookback window? (Select one!)
AP14D
B14Days
CPT336H
D14D
Explanation
P14D is the correct ISO 8601 duration string for a 14-day period. ISO 8601 duration format mandates the period designator P as a prefix, followed by numeric values and unit designators. For day-based windows, the format is P followed by the number and the D unit designator. Azure ML model monitoring uses this format for all lookback window configuration, where P7D represents 7 days and P30D represents 30 days. The value 14D omits the mandatory P prefix and is not valid ISO 8601. PT336H technically equals 14 days expressed in hours using the time component designator T, but is not the standard or recommended format for day-based Azure ML monitoring windows. The value 14Days is not valid ISO 8601 syntax and will be rejected by the monitoring configuration.
5. Adatum Corp's data science team uses Azure Machine Learning AutoML to build a binary classification model predicting equipment failures. AutoML generates a Stacking ensemble as one of the top candidate models. Which algorithm does AutoML use as the meta-learner in the Stacking ensemble for a classification task? (Select one!)
AElasticNet
BLogistic Regression
CGradient Boosting Machine
DRandom Forest Classifier
Explanation
For classification tasks, AutoML's Stacking ensemble uses Logistic Regression as the meta-learner — the second-level model that blends predictions generated by the base-level models. The base models produce predictions that are fed as input features into the Logistic Regression meta-learner to produce the final ensemble output. For regression and forecasting tasks, AutoML uses ElasticNet as the meta-learner instead of Logistic Regression. The Voting ensemble is a distinct approach that combines predictions by computing a weighted average of predicted probabilities across base models, without training a separate second-level learner.
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