100+ Free AI-300 Practice Questions

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You are creating a new Azure Machine Learning workspace for an MLOps team. Which Azure resource is automatically provisioned as a dependency when you create a Machine Learning workspace?

Azure Container Registry, Azure Storage account, Azure Key Vault, and Application Insights

Azure SQL Database and Azure Functions

Azure Cosmos DB and Azure API Management

Azure Synapse Analytics and Azure Data Factory

to track

2026 Statistics

Key Facts: AI-300 Exam

700/1000

Passing Score

Microsoft

40-60 Q

Typical Questions

Microsoft

100 min

Exam Duration

Microsoft

$165

US Exam Fee

Microsoft

5 domains

Skills Areas

Study guide

Annual

Free Renewal

Microsoft Learn

AI-300 is a 2026 associate-level Microsoft certification for MLOps engineers. Expect roughly 40-60 questions in 100 minutes, a 700/1000 passing score, and five domains covering MLOps infrastructure (15-20%), model lifecycle (25-30%), GenAIOps infrastructure (20-25%), quality and observability (10-15%), and optimization (10-15%).

Sample AI-300 Practice Questions

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

1You are creating a new Azure Machine Learning workspace for an MLOps team. Which Azure resource is automatically provisioned as a dependency when you create a Machine Learning workspace?

A.Azure Container Registry, Azure Storage account, Azure Key Vault, and Application Insights

B.Azure SQL Database and Azure Functions

C.Azure Cosmos DB and Azure API Management

D.Azure Synapse Analytics and Azure Data Factory

Explanation: When you create an Azure Machine Learning workspace, Azure automatically provisions four dependent resources: an Azure Storage account (for datastores and artifacts), an Azure Key Vault (for secrets), an Application Insights instance (for telemetry), and an Azure Container Registry (created on first image build for environments). These dependencies are required for workspace functionality.

2An MLOps engineer needs to register an external Azure Blob Storage container as a datastore in an Azure Machine Learning workspace. Which authentication method is recommended for production workloads following least-privilege principles?

A.Account key stored in the datastore configuration

B.Workspace managed identity with RBAC role on the storage account

C.SAS token stored in plain text

D.Anonymous public access

Explanation: For production datastores, you should use the workspace's system-assigned or user-assigned managed identity and grant the appropriate RBAC role (e.g., Storage Blob Data Reader) on the target storage account. This avoids embedded secrets, supports key rotation, and follows least-privilege. Account keys and SAS tokens are supported but discouraged for production.

3Which Azure Machine Learning compute type is most cost-effective for a long-running training job that requires GPUs and scales out across multiple nodes?

A.Compute instance

B.Serverless compute (managed compute)

C.Compute cluster

D.Attached Synapse Spark pool

Explanation: An Azure Machine Learning compute cluster supports auto-scaling between a minimum and maximum number of nodes, supports GPU SKUs, and shuts down idle nodes to control cost. Compute instances are single-node dev environments. Serverless compute is convenient but offers less control over node lifecycle. Synapse Spark pools target big-data ETL, not multi-node deep learning training.

4You want to deploy an Azure Machine Learning workspace and its dependent resources using infrastructure as code. According to the AI-300 study guide, which two technologies should you use?

A.Terraform and PowerShell DSC

B.Bicep and Azure CLI

C.ARM templates only

D.Pulumi and kubectl

Explanation: The AI-300 skills measured explicitly call out deploying Machine Learning workspaces and resources by using Bicep and Azure CLI. While Terraform and ARM templates are valid Azure IaC tools, the certification objectives focus on Bicep + Azure CLI as the recommended path for declarative Microsoft-native deployments.

5Which GitHub Actions trigger should an MLOps engineer use to retrain a model whenever new training data is committed to a specific folder in the repository?

A.schedule with a cron expression

B.workflow_dispatch only

C.push with a paths filter targeting the data folder

D.release event

Explanation: A push trigger with a paths filter (for example, paths: ['data/training/**']) fires the workflow only when files in that folder change. This is the idiomatic CI trigger for data-driven retraining. Schedule triggers run on a clock, workflow_dispatch is manual, and release triggers fire on tagged releases.

6To restrict network access to an Azure Machine Learning workspace so that traffic from the public internet is denied, which configuration should you apply?

A.Enable a firewall rule that allows all Azure IP ranges

B.Configure a managed virtual network with public network access disabled and use private endpoints

C.Add a service tag exception in the workspace storage account

D.Rotate the workspace primary key

Explanation: Azure Machine Learning supports a managed virtual network with public network access disabled, combined with private endpoints for the workspace and dependent resources. This is the recommended pattern to restrict network access. Firewall allow-lists for Azure IP ranges are too permissive, and key rotation does not affect network exposure.

7Which Azure Machine Learning entity packages the Python dependencies, base image, and conda specification needed for a job to run?

A.Component

B.Environment

C.Datastore

D.Endpoint

Explanation: An Environment in Azure Machine Learning encapsulates the Docker base image, conda or pip dependencies, and environment variables required to execute training or inference code. Components are reusable pipeline steps, datastores are storage references, and endpoints serve deployed models.

8An MLOps engineer wants to share a curated training dataset across multiple Azure Machine Learning workspaces in different regions. Which feature should they use?

A.Azure Machine Learning registry

B.Azure Blob storage SAS token

C.Workspace data labeling project

D.Compute instance shared disk

Explanation: Azure Machine Learning registries enable sharing of assets such as data, environments, components, and models across workspaces and regions. They serve as a central catalog, supporting cross-workspace MLOps without copying assets manually.

9When configuring identity and access management for an Azure Machine Learning workspace, which built-in role grants the ability to submit jobs and manage assets but not modify workspace-level settings?

A.Owner

B.AzureML Compute Operator

C.AzureML Data Scientist

D.Reader

Explanation: The AzureML Data Scientist role allows users to perform all actions within the workspace except creating or deleting compute resources or modifying workspace settings. Owner grants full control, AzureML Compute Operator only manages compute, and Reader is read-only.

10You need to capture a training script's parameters, metrics, and output artifacts in a way that is portable across MLOps tools. Which open-source framework does Azure Machine Learning natively integrate with for experiment tracking?

A.TensorBoard only

B.MLflow

C.Weights and Biases only

D.Comet ML

Explanation: Azure Machine Learning provides native MLflow tracking integration. The workspace acts as the MLflow tracking server, so calls to mlflow.log_param, mlflow.log_metric, and mlflow.log_artifact route to the workspace. This makes experiments portable across local, Databricks, and Azure ML environments.

About the AI-300 Exam

The AI-300 exam validates the skills needed to set up, operate, and optimize MLOps and GenAIOps infrastructure on Azure using Azure Machine Learning, Microsoft Foundry, GitHub Actions, Bicep, and Azure CLI across the model and generative AI application lifecycle.

Questions

50 scored questions

Time Limit

100 minutes

Passing Score

700/1000

Exam Fee

$165 USD (Microsoft / Pearson VUE)

AI-300 Exam Content Outline

15-20%

Design and implement an MLOps infrastructure

Create and manage Azure Machine Learning workspaces, datastores, compute targets, environments, components, registries, and identity. Implement IaC with Bicep, Azure CLI, and GitHub Actions, and restrict network access using managed VNets and private endpoints.

25-30%

Implement machine learning model lifecycle and operations

Orchestrate model training with MLflow tracking, AutoML, hyperparameter sweeps, and distributed training pipelines. Register and version MLflow models, evaluate with responsible AI principles, deploy real-time and batch endpoints, and monitor for data drift.

20-25%

Design and implement a GenAIOps infrastructure

Provision Microsoft Foundry hubs, projects, connections, managed identities, and private networking with Bicep. Deploy foundation models via serverless API endpoints, managed compute, and provisioned throughput units. Implement prompt versioning and variant management with Git.

10-15%

Implement generative AI quality assurance and observability

Configure evaluation datasets and built-in evaluators (groundedness, relevance, coherence, fluency) plus risk and safety evaluators. Implement continuous monitoring, distributed tracing, latency and token-usage metrics, and detailed logging for production troubleshooting.

10-15%

Optimize generative AI systems and model performance

Tune retrieval performance with chunking, similarity thresholds, and hybrid search. Select and fine-tune embedding models, implement LoRA and full fine-tuning, manage synthetic data generation, and promote fine-tuned models from development to production.

How to Pass the AI-300 Exam

What You Need to Know

Passing score: 700/1000
Exam length: 50 questions
Time limit: 100 minutes
Exam fee: $165 USD

Keys to Passing

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

AI-300 Study Tips from Top Performers

1Build at least one end-to-end MLOps project: a GitHub Actions workflow that uses Bicep to deploy an Azure ML workspace, runs a training pipeline with MLflow tracking, registers a model, and deploys to a managed online endpoint.

2Stand up a Microsoft Foundry hub and project, then build a prompt flow that uses Azure OpenAI and Azure AI Search with continuous evaluation enabled so you understand the GenAIOps loop.

3Memorize the AI-300 domain weightings and which lifecycle activities live in each, especially the difference between the MLOps lifecycle (25-30%) and the GenAIOps infrastructure (20-25%) domains.

4Practice both the Azure ML CLI v2 (az ml ... -f job.yaml) and the Python SDK v2; the exam can test either flavor of declarative job submission.

5Get fluent with managed online endpoints versus batch endpoints, including traffic splits, progressive rollout, autoscaling, and rollback patterns.

6Memorize the built-in Foundry evaluators (groundedness, relevance, coherence, fluency, similarity, F1, plus risk and safety evaluators) and when each applies.

Frequently Asked Questions

What is the AI-300 exam?

AI-300 is the Microsoft exam for the Azure MLOps Engineer Associate credential. It validates the ability to operationalize machine learning and generative AI solutions on Azure by using Azure Machine Learning, Microsoft Foundry, GitHub Actions, Bicep, and Azure CLI across the full lifecycle from infrastructure to deployment, monitoring, and optimization.

How many questions are on AI-300 and how long do you get?

Microsoft role-based associate exams typically deliver about 40-60 questions. For AI-300, plan for a 100-minute exam duration and a 700 out of 1000 passing score. The total seat time including instructions is roughly 120 minutes.

What are the AI-300 domains and weightings?

AI-300 covers five domains: Design and implement an MLOps infrastructure (15-20%), Implement machine learning model lifecycle and operations (25-30%), Design and implement a GenAIOps infrastructure (20-25%), Implement generative AI quality assurance and observability (10-15%), and Optimize generative AI systems and model performance (10-15%).

How hard is AI-300?

AI-300 is a challenging associate exam. It expects practical experience with Azure Machine Learning workspaces, MLflow, distributed training, managed online and batch endpoints, Microsoft Foundry hubs and projects, prompt flow, evaluators, GitHub Actions, Bicep, and Azure CLI. Reading docs is not enough; you should have built and deployed end-to-end solutions.

How long should I study for AI-300?

Plan for about 80-140 hours over 6-10 weeks depending on prior Azure ML and GenAIOps experience. Effective preparation includes building MLOps pipelines with Azure ML SDK v2, deploying online and batch endpoints, building a Foundry project with prompt flow and continuous evaluation, automating with Bicep and GitHub Actions, and completing 100+ practice questions.

Does AI-300 certification expire?

Yes. Microsoft associate certifications, including AI-300, expire 12 months after you earn them. You can renew at no cost by passing a free online renewal assessment on Microsoft Learn before the expiration date.