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Microsoft AI-300 : Operationalizing Machine Learning and Generative AI Solutions

AI-300

Exam Code: AI-300

Exam Name: Operationalizing Machine Learning and Generative AI Solutions

Updated: Aug 31, 2026

Q & A: 188 Questions and Answers

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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Design and implement an MLOps infrastructure- Manage environments, data stores, and model registries
- Implement security, governance, and compliance for MLOps
- Configure source control, CI/CD pipelines, and automation for ML workflows
- Set up Azure Machine Learning workspace and compute targets
Implement machine learning model lifecycle and operations- Retrain, update, and manage model versions in production
- Deploy models to real-time and batch endpoints
- Monitor model performance, data drift, and operational health
- Train, register, and version models using Azure Machine Learning
Design and implement a GenAIOps infrastructure- Set up Microsoft Foundry and Azure AI services for generative AI workloads
- Configure prompt orchestration, prompt flows, and agent frameworks
- Manage API keys, rate limits, and responsible AI guardrails
- Implement RAG (Retrieval-Augmented Generation) pipelines and vector search
Optimize generative AI systems and model performance- Tune prompts, system messages, and grounding strategies
- Fine-tune and distill models for specific use cases
- Implement cost management and scaling strategies for GenAI workloads
- Optimize inference performance, caching, and throughput
Implement generative AI quality assurance and observability- Evaluate generative AI outputs for quality, safety, and grounding
- Monitor latency, token usage, cost, and error rates
- Conduct red teaming, adversarial testing, and content filtering
- Implement logging, tracing, and telemetry for GenAI applications

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
- accounts for the performance of all previous runs when evaluating the current run
- avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Median stopping
B. Bandit
C. Default
D. Truncation selection


Question 2

A company has multiple data science teams working on separate machine learning projects.
The company requires models to be auditable, reusable, and governed centrally across teams.
The models must allow team-level isolation for billing.
You need to establish the foundation for governed machine learning operations.
Which action should you perform first?

A. Create a shared hub workspace and project workspaces for each team.
B. Register shared datasets in a central storage account.
C. Create a resource group for shared machine learning assets.
D. Create a shared Azure Machine Learning workspace.


Question 3

A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A. Tag the training experiment with a name.
B. Export the model files to local storage.
C. Locate and capture the model artifacts from the outputs of the training run.
D. Register the model in the Azure Machine Learning workspace.


Question 4

A team is building a Retrieval-Augmented Generation (RAG) system.
The team observes that the retrieved documents are often irrelevant or incomplete.
You need to improve retrieval accuracy.
What should you adjust?

A. Chunk size and overlap
B. Token limits
C. Temperature parameter
D. Embedding strategy


Question 5

You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?

A. Enable tracing for all inference calls in the evaluation pipeline.
B. Deploy the base model to an online endpoint before starting fine-tuning.
C. Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
D. Use prompt flow to generate multiple prompt templates for evaluation.


Solutions:

Question 1
Answer: A,D
Question 2
Answer: A
Question 3
Answer: C,D
Question 4
Answer: A
Question 5
Answer: A

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