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100+ Free Microsoft AI-300 MLOps Engineer Practice Questions

Pass your Microsoft Certified: Machine Learning Operations Engineer Associate (Exam AI-300) exam on the first try — instant access, no signup required.

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Your MLOps team wants the same Bicep deployment to create dev, test, and prod Azure Machine Learning workspaces with environment-specific settings. What is the best approach?

A
B
C
D
to track
2026 Statistics

Key Facts: Microsoft AI-300 MLOps Engineer Exam

$165

Exam Fee (USD)

Microsoft

120 min

Exam Duration

Microsoft

700/1000

Passing Score

Microsoft

40-60

Approx. Questions

Microsoft

Pearson VUE

Exam Provider

Microsoft

June 1, 2026

DP-100 Retirement

Microsoft

As of May 2026, Microsoft lists Exam AI-300 (Operationalizing Machine Learning and Generative AI Solutions) as a role-based associate exam scheduled through Pearson VUE for about $165 USD, with 120 minutes to complete it and a 700-out-of-1000 score required to pass. The largest skill area is Implement machine learning model lifecycle and operations at 25-30%, followed by Design and implement a GenAIOps infrastructure at 20-25%, Design and implement an MLOps infrastructure at 15-20%, Implement generative AI quality assurance and observability at 10-15%, and Optimize generative AI systems and model performance at 10-15%. AI-300 replaces DP-100, which retires June 1, 2026.

Sample Microsoft AI-300 MLOps Engineer Practice Questions

Try these sample questions to test your Microsoft AI-300 MLOps Engineer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In Azure Machine Learning, what is the top-level resource that groups experiments, models, endpoints, datastores, and compute together?
A.A resource group
B.A workspace
C.A subscription
D.A datastore
Explanation: The Azure Machine Learning workspace is the central, top-level resource that organizes experiments, jobs, models, environments, datastores, and compute. A resource group and subscription are broader Azure containers, while a datastore is just one asset inside the workspace.
2Which Azure Machine Learning asset abstracts a connection to an Azure storage account so jobs can access data without embedding credentials in code?
A.Compute target
B.Datastore
C.Environment
D.Component
Explanation: A datastore is a workspace asset that securely references an Azure storage service such as Blob Storage or Data Lake, keeping connection details and credentials out of training code. Data assets then point to specific paths within a datastore.
3What is the main advantage of an Azure Machine Learning compute cluster over a single compute instance for training jobs?
A.It provides a personal development VM with a Jupyter UI
B.It autoscales nodes up and down for parallel and on-demand training workloads
C.It is required for deploying real-time inference endpoints
D.It permanently runs one node so jobs start instantly at all times
Explanation: A compute cluster is a managed, multi-node compute target that scales out for parallel training and scales down to zero when idle, controlling cost. A compute instance is a single, personal development machine more suited to authoring and notebooks.
4You want jobs in your Azure Machine Learning workspace to access a storage account and Key Vault without storing secrets. Which identity approach should you configure?
A.A shared access signature token hardcoded in the script
B.A managed identity for the workspace with role-based access control
C.A personal access token stored in the notebook
D.An admin username and password in an environment variable
Explanation: Configuring a managed identity for the workspace and granting it the right Azure RBAC roles lets jobs authenticate to storage, Key Vault, and other resources without any stored secrets. This is the recommended secure access pattern for MLOps.
5Which Azure Machine Learning asset captures the software dependencies, base image, and Python packages needed to run a job reproducibly?
A.Data asset
B.Environment
C.Datastore
D.Endpoint
Explanation: An environment defines the runtime context for a job, including the base Docker image, Conda or pip dependencies, and environment variables, so training and inference run reproducibly. Environments are versioned assets you can reuse across jobs.
6What is the primary purpose of a reusable component in an Azure Machine Learning pipeline?
A.To store the workspace's billing configuration
B.To package a single self-contained step with defined inputs, outputs, and code for reuse
C.To replace the need for a compute target
D.To act as the real-time scoring endpoint
Explanation: A component is a self-contained, versioned pipeline step that declares its inputs, outputs, command, and environment, making it reusable and composable across pipelines. Components are the building blocks that let teams standardize and share training and processing steps.
7Your organization needs to share curated models and components across multiple Azure Machine Learning workspaces. Which feature should you use?
A.A workspace-scoped datastore
B.An Azure Machine Learning registry
C.A local pip cache
D.A separate resource group per asset
Explanation: An Azure Machine Learning registry is a cross-workspace catalog that lets you share models, components, and environments across many workspaces and regions. This promotes reuse and consistent governance in larger MLOps organizations.
8Which infrastructure-as-code approach does the AI-300 audience profile expect for deploying Azure Machine Learning workspaces and resources?
A.Clicking through the Azure portal manually for each deployment
B.Authoring Bicep templates and deploying with the Azure CLI
C.Editing the ARM state file directly in storage
D.Copying resources between subscriptions by hand
Explanation: The AI-300 study guide expects candidates to deploy Machine Learning workspaces and resources using Bicep templates and the Azure CLI as infrastructure as code. This makes provisioning repeatable, version-controlled, and auditable.
9You want to automatically provision Azure Machine Learning resources whenever code is merged to the main branch of your repository. Which tool fits the AI-300 IaC workflow?
A.A nightly manual checklist run by an engineer
B.GitHub Actions workflows triggered by repository events
C.A spreadsheet of resource names
D.An email approval chain
Explanation: GitHub Actions workflows can run on repository events such as a push or merge to main and execute Bicep or Azure CLI steps to provision resources automatically. This is the continuous-delivery automation pattern the AI-300 study guide describes for MLOps.
10To prevent public internet access to an Azure Machine Learning workspace and keep traffic on your virtual network, what should you configure?
A.A larger compute cluster
B.A private endpoint with managed virtual network isolation
C.A second datastore
D.More MLflow experiments
Explanation: Restricting network access is achieved by placing the workspace behind a private endpoint and using managed virtual network isolation so traffic stays on private IP space. This is the recommended way to lock down a workspace for secure MLOps.

About the Microsoft AI-300 MLOps Engineer Exam

Microsoft's AI-300 exam validates a Machine Learning Operations Engineer who builds and operates MLOps and GenAIOps (together, AIOps) infrastructure on Azure. The blueprint spans Azure Machine Learning workspaces and pipelines, MLflow tracking, managed online and batch endpoints, data drift monitoring, Microsoft Foundry foundation-model deployment, prompt versioning, generative AI evaluation and observability, and RAG and fine-tuning optimization. AI-300 replaces the retiring DP-100 exam.

Questions

50 scored questions

Time Limit

120 minutes

Passing Score

700/1000

Exam Fee

$165 (Microsoft)

Microsoft AI-300 MLOps Engineer Exam Content Outline

15-20%

Design and implement an MLOps infrastructure

Create and manage Azure Machine Learning workspaces, datastores, and compute targets; build data assets, environments, and components; and implement IaC with Bicep, Azure CLI, GitHub integration, and GitHub Actions while restricting network access.

25-30%

Implement machine learning model lifecycle and operations

Orchestrate training with MLflow experiment tracking, AutoML, hyperparameter sweeps, and pipelines; register and version MLflow models with responsible AI evaluation; deploy real-time and batch endpoints with safe rollout; and detect data drift with retraining triggers.

20-25%

Design and implement a GenAIOps infrastructure

Configure Microsoft Foundry resources, projects, managed identities, RBAC, and private networking; deploy foundation models via serverless API endpoints, managed compute, and provisioned throughput; and version and compare prompts using Git repositories.

10-15%

Implement generative AI quality assurance and observability

Create test datasets and data mapping; apply AI quality metrics including groundedness, relevance, coherence, and fluency; configure risk and safety evaluations; and monitor latency, throughput, token cost, logging, and tracing in Foundry.

10-15%

Optimize generative AI systems and model performance

Tune RAG retrieval through chunk size, similarity thresholds, and hybrid semantic plus keyword search; select and fine-tune embedding models; and apply advanced fine-tuning with synthetic data, then promote fine-tuned models to production.

How to Pass the Microsoft AI-300 MLOps Engineer Exam

What You Need to Know

  • Passing score: 700/1000
  • Exam length: 50 questions
  • Time limit: 120 minutes
  • Exam fee: $165

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Microsoft AI-300 MLOps Engineer Study Tips from Top Performers

1Spend the most prep time on Implement machine learning model lifecycle and operations because it is the single largest skill area at 25-30% of the exam.
2Practice the full Azure Machine Learning lifecycle in a real workspace: register an MLflow model, deploy a managed online endpoint, shift traffic for blue-green rollout, then roll back safely.
3Know the difference between managed online endpoints for real-time inference and batch endpoints for scoring large datasets asynchronously, including when to choose each.
4Be fluent in Microsoft Foundry GenAIOps: project resources, serverless API versus managed compute deployment, provisioned throughput units, and Git-based prompt versioning and comparison.
5Memorize the generative AI quality metrics by name: groundedness, relevance, coherence, and fluency, plus risk and safety evaluations for harmful content.
6For optimization questions, connect RAG tuning levers (chunk size, similarity threshold, hybrid search, embedding model) and fine-tuning with synthetic data to specific accuracy or cost outcomes.

Frequently Asked Questions

What are the official exam facts for Microsoft AI-300?

AI-300 is a role-based associate exam scheduled through Pearson VUE for about $165 USD. You have 120 minutes to complete it, and a scaled score of 700 out of 1000 is required to pass. Microsoft typically presents 40 to 60 questions, including possible case studies and interactive items.

Does AI-300 replace DP-100?

Yes. AI-300, Operationalizing Machine Learning and Generative AI Solutions, is the successor to the Azure Data Scientist Associate exam DP-100, which Microsoft retires on June 1, 2026. AI-300 adds substantial generative AI operations content that DP-100 did not cover.

What skills are weighted most heavily on AI-300?

Implement machine learning model lifecycle and operations is the largest area at 25-30%. Design and implement a GenAIOps infrastructure is 20-25%, Design and implement an MLOps infrastructure is 15-20%, and the two generative AI quality and optimization areas are each 10-15%.

Which Azure services does AI-300 focus on?

The exam centers on Azure Machine Learning for traditional MLOps and Microsoft Foundry for generative AI operations. You should know MLflow, managed online and batch endpoints, data drift monitoring, GitHub Actions, Bicep, prompt flow, evaluations, and retrieval-augmented generation.

How long does the AI-300 certification stay valid?

Microsoft associate certifications expire one year after you earn them. You renew for free by passing a short online assessment on Microsoft Learn during the six months before the expiration date.

What is the best way to prepare for AI-300?

Get hands-on with Azure Machine Learning and Microsoft Foundry, then drill mixed practice questions across all five skill areas. Because model lifecycle and operations carries the most weight, spend the largest share of study time on MLflow tracking, endpoints, and drift monitoring.