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AI-300 Operationalizing Machine Learning and Generative AI Solutions Exam Topics and Questions

These Microsoft Operationalizing Machine Learning and Generative AI Solutions (AI-300) exam topics are organized according to official exam domains to help candidates quickly verify coverage and focus on assessment rather than theory. Each domain is paired with topic-wise AI-300 sample questions that reflect how objectives are tested in the actual exam. This structure enables efficient review, targeted self-assessment, and rapid identification of weak areas when preparing for the Microsoft Operationalizing Machine Learning and Generative AI Solutions certification exam.

Let's Practice Free Microsoft AI-300 Questions Aligned with Official Exam Topics

Follows Microsoft's official outline Updated 11 Sep, 2026 5 Topics
Reviewed by Olivia Harris, Microsoft AI-300 Certified Professional
Topic Content
Design and implement an MLOps infrastructure by establishing foundational workspace management capabilities including creating and managing workspaces, datastores, and compute targets while configuring proper identity and access management controls. Build and organize machine learning assets within the workspace environment by creating and managing data assets, environments, and components, with the ability to share these assets across multiple workspaces through registry systems. Implement Infrastructure as Code practices for machine learning operations by configuring GitHub integration for secure access, deploying workspaces and... See More
Sample Questions for Topic 1 : Design and implement an MLOps infrastructure
Q1

An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.

An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.

You need to change the state of the model version to meet the requirements.

What should you do?

Topic Content
Orchestrate model training by configuring experiment tracking with MLflow, using automated machine learning to explore optimal models, leveraging notebooks for experimentation and exploration, automating hyperparameter tuning, running model training scripts, managing distributed training for large and deep learning models, implementing training pipelines, and comparing model performance across jobs. Implement model registration and versioning by packaging feature retrieval specifications with model artifacts, registering MLflow models, evaluating models using responsible AI principles, and managing the complete model lifecycle including archiving. Deploy machine... See More
Sample Questions for Topic 2 : Implement machine learning model lifecycle and operations
Q2

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.

You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data.

The training_data argument specifies the path to the training data in a file named dataset1.csv.

You plan to run the script.py Python script as a command job that trains a machine learning model.

You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.

Solution: python script.py dataset1.csv

Does the solution meet the goal?

Topic Content
Design and implement a GenAIOps infrastructure by establishing Foundry environments and platform configurations, including creating and configuring Foundry resources, project environments, identity and access management with managed identities and RBAC, network security with private networking, and deploying infrastructure through Bicep templates and Azure CLI. Deploy and manage foundation models for production workloads by utilizing serverless API endpoints and managed compute options, selecting appropriate models for specific use cases, implementing model versioning and production deployment strategies, and configuring provisioned throughput units... See More
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Topic Content
Implement generative AI quality assurance and observability encompasses two critical areas establishing robust evaluation and validation frameworks, and deploying comprehensive monitoring systems. For evaluation and validation, you will create specialized test datasets with proper data mapping to enable thorough model assessment, implement quality metrics such as groundedness, relevance, coherence, and fluency to measure output quality, configure risk and safety evaluations to detect harmful content, and establish automated evaluation workflows utilizing both built-in and custom evaluation metrics. For observability, you will... See More
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Topic Content
Optimize Retrieval-Augmented Generation RAG Performance and Accuracy Enhance RAG system effectiveness through strategic tuning of similarity thresholds, chunk sizes, and retrieval strategies to maximize retrieval performance. Select and fine-tune embedding models tailored to domain-specific requirements, ensuring improved accuracy and relevance in generated responses. Implement hybrid search approaches that seamlessly combine semantic and keyword-based retrieval methods for comprehensive information discovery. Evaluate RAG system performance using relevance metrics and AB testing frameworks to identify optimization opportunities and measure improvements. Monitor system behavior across... See More
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