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's AI engineering team is operationalizing a customer service assistant built on Azure AI Foundry. During an internal architecture review, the tech lead claims that Azure AI Foundry natively stores and versions prompts as managed assets within the Foundry resource, similar to how Azure Machine Learning stores registered models. A junior engineer challenges this claim and proposes using Git for prompt versioning instead. Which statement correctly describes how prompt versioning should be handled in Azure AI Foundry? (Select one!)
APrompt versions are created automatically in Foundry each time the temperature or top_p parameters are changed in the playground
BPrompts are not stored as versioned assets in Azure AI Foundry natively and must be versioned externally in Git repositories, with configuration changes such as temperature adjustments constituting a new prompt version
CAzure AI Foundry includes a built-in prompt registry that automatically versions prompts whenever the system prompt or configuration is modified
DPrompts can be versioned using the Azure Machine Learning model registry by packaging them as MLflow artifacts, which Foundry natively reads
Explanation
Azure AI Foundry does not provide a native versioned prompt registry. Teams are responsible for implementing prompt versioning externally, and the recommended approach is to treat prompt files as code stored in Git repositories. This enables branching, pull requests, history tracking, and rollback. Any configuration change — including adjusting temperature from 0.3 to 0.7 — constitutes a new prompt version and should be committed to Git with an appropriate tag or release. The Azure Machine Learning model registry is designed for trained model artifacts, not text prompt files, and is not a recommended path for prompt versioning. Foundry does not automatically create versions on parameter changes in the playground.
2. Adatum Corporation's legal team has identified a compliance risk with their Microsoft Foundry RAG system used for internal policy document Q&A. Users are receiving responses that are factually accurate and fully supported by the grounding context, but critical clauses from source policies are frequently omitted from the answers. This incompleteness creates audit exposure when users rely on partial policy information. The AI quality team wants to add a single evaluation metric to their automated pipeline to detect and track this coverage gap. Which metric should they add? (Select one!)
ARetrieval, which measures whether the context chunks retrieved from the vector index are sufficiently relevant to the user query before response generation
BResponse Completeness, which measures on a 1-5 scale how thoroughly the response covers all critical information present in the ground truth answer
CFluency, which measures whether the response uses grammatically correct and natural-sounding language appropriate for formal policy communication
DGroundedness, which measures on a 1-5 scale whether the response introduces any content not present in the grounding context, catching hallucinations and unsupported claims
Explanation
Response Completeness is the recall-oriented metric in the RAG evaluation suite. It operates on a 1-5 scale and specifically measures whether the generated response covers all critical information found in the ground truth, making it the correct metric for detecting omissions of legally significant content. The compliance scenario describes exactly the problem Response Completeness is designed to catch: accurate but incomplete responses that miss important clauses. Groundedness is the precision-oriented complement — it detects when responses include content not present in the grounding context, but it evaluates only what was said and cannot detect what was omitted. Retrieval measures the relevance quality of retrieved chunks before generation occurs and would not catch post-retrieval omissions during response synthesis. Fluency evaluates grammatical correctness and natural language quality, which has no relationship to information coverage or completeness.
3. Blue Yonder Airlines' AI team needs to process large-scale flight scheduling recommendations using a language model deployed through Microsoft Foundry. Queries are submitted at 11 PM nightly and results must be available by 8 AM the following morning. The team must minimize inference cost and has no real-time latency requirement. Which Foundry deployment SKU best meets these requirements? (Select one!)
AGlobalStandard — pay-per-token pricing with the highest default quota, optimized for real-time synchronous inference
BGlobalProvisionedManaged — consistent low-latency inference with hourly billing per provisioned throughput unit starting at deployment creation regardless of usage
CGlobalBatch — approximately 50% cost reduction compared to standard pay-per-token pricing with a 24-hour target turnaround and no real-time SLA
DDataZoneStandard — pay-per-token pricing that restricts data residency to US and EU geographic zones for compliance requirements
Explanation
GlobalBatch is the correct choice for this overnight batch workload because it offers approximately 50% cost savings compared to standard pricing and targets a 24-hour turnaround, which is well within the 9-hour overnight processing window. Batch deployments are designed specifically for non-real-time, high-volume workloads where consistent low latency is unnecessary. GlobalStandard is optimized for real-time synchronous inference and carries a higher per-token cost that is not justified for batch processing. GlobalProvisionedManaged uses hourly billing per provisioned throughput unit that begins immediately at deployment creation regardless of whether the deployment is actively serving requests, making it expensive for a workload with no continuous traffic throughout the day. DataZoneStandard is a pay-per-token deployment option for organizations with US/EU data residency compliance requirements, not a cost-optimization strategy.
4. Tailspin Toys' AI quality team is configuring an evaluation run for their customer support RAG system in Azure AI Foundry. Their JSONL test dataset uses the field name customer_query for the user question and retrieved_passages for the grounding context. After mapping these fields to the groundedness evaluator and submitting the run, the evaluation fails immediately without processing any rows. What is the most likely cause? (Select one!)
AThe groundedness evaluator requires CSV format; JSONL is not a supported input format
BField names containing underscores are not valid in Azure AI Foundry data mapping syntax
CThe source field retrieved_passages must be renamed to context in the dataset before mapping
DA required field in the groundedness evaluator was left unmapped, causing the evaluator to fail entirely before processing any rows
Explanation
In Azure AI Foundry evaluation pipelines, required evaluator fields are marked with an asterisk and must be explicitly mapped before a run can proceed. If any required field is left unassigned, the evaluator fails entirely and does not process any rows—this is a hard failure, not a warning or partial result. Both CSV and JSONL are supported formats for evaluation test datasets, so the file format is not the cause. Field names with underscores are fully supported using the data mapping syntax {{item.field_name}}. There is no requirement to rename source fields in the dataset; the mapping layer exists precisely to bind arbitrarily named source fields to evaluator inputs.
5. Litware's platform team is configuring CI/CD for Azure Machine Learning using GitHub Actions with OIDC federated credentials to avoid storing secrets in GitHub. The workflow includes the azure/login@v2 action and a correctly configured federated credential in Entra ID. However, authentication silently fails every time the workflow runs and no error message appears in the login step output. What is the most likely cause of this failure? (Select one!)
AThe workflow YAML is missing the id-token: write permission in the permissions block
BThe Azure Machine Learning workspace was not configured to allow GitHub Actions runners as trusted callers
COIDC federated credentials must be bound to a user-assigned managed identity rather than a service principal
DThe ml CLI extension was not installed in the job before the azure/login step executed
Explanation
OIDC authentication in GitHub Actions requires the id-token: write permission to be explicitly declared in the workflow YAML permissions block. Without this permission, the GitHub Actions runner cannot request an OIDC token from GitHub's identity provider, causing azure/login@v2 to fail silently without a visible error message. The ml CLI extension must be installed for ml commands to work, but its absence produces a command-not-found error during ml command execution rather than a silent failure at the authentication step itself. Azure Machine Learning workspaces do not require special configuration to trust GitHub Actions runners; trust is established through the federated credential configuration in Entra ID. OIDC federated credentials are fully compatible with service principal authentication and do not require user-assigned managed identities.
More Microsoft Practice Exams
Microsoft Certified: Azure AI Cloud Developer Associate (AI-200)
AI-200 · 600 questions
Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)
DP-750 · 593 questions
Microsoft Certified: Intelligent Applications Builder Associate (AB-410)
AB-410 · 600 questions
Microsoft Certified: SQL AI Developer Associate (DP-800)
DP-800 · 600 questions
Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate (MS-721)
MS-721 · 306 questions
Microsoft Certified: Azure Virtual Desktop Specialty (AZ-140)
AZ-140 · 517 questions
$17.99
One-time access to this exam
583 questions (11 practice exams' worth)
Unlimited timed exam simulations
Yours forever — no subscription
Detailed explanations
Free preview stays available