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

AI-300 real exams

Exam Code: AI-300

Exam Name: Operationalizing Machine Learning and Generative AI Solutions

Updated: Jul 21, 2026

Q & A: 159 Questions and Answers

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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Topic 1: Design and implement an MLOps infrastructure- Set up Azure Machine Learning workspace and compute targets
- Implement security, governance, and compliance for MLOps
- Manage environments, data stores, and model registries
- Configure source control, CI/CD pipelines, and automation for ML workflows
Topic 2: Optimize generative AI systems and model performance- Fine-tune and distill models for specific use cases
- Tune prompts, system messages, and grounding strategies
- Optimize inference performance, caching, and throughput
- Implement cost management and scaling strategies for GenAI workloads
Topic 3: 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
- Implement RAG (Retrieval-Augmented Generation) pipelines and vector search
- Manage API keys, rate limits, and responsible AI guardrails
Topic 4: Implement generative AI quality assurance and observability- Evaluate generative AI outputs for quality, safety, and grounding
- Conduct red teaming, adversarial testing, and content filtering
- Monitor latency, token usage, cost, and error rates
- Implement logging, tracing, and telemetry for GenAI applications
Topic 5: Implement machine learning model lifecycle and operations- Deploy models to real-time and batch endpoints
- Retrain, update, and manage model versions in production
- Train, register, and version models using Azure Machine Learning
- Monitor model performance, data drift, and operational health

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Hotspot Question
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


2. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

A) Increase timeout settings
B) Reduce logging
C) Set minimum instance count
D) Switch to batch endpoint


3. Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point


4. Hotspot Question
A regression model is deployed to a production online endpoint and is actively serving predictions.
Business stakeholders report that the model's prediction quality has changed.
The endpoint remains available and responsive.
You need to determine which metric to examine to validate each quality concern.
Which metric should you review for each concern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


5. You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?

A) Traffic splitting with monitoring alerts
B) Batch endpoint validation
C) Manual testing workflow
D) Separate endpoint for testing


Solutions:

Question # 1
Answer: Only visible for members
Question # 2
Answer: C
Question # 3
Answer: Only visible for members
Question # 4
Answer: Only visible for members
Question # 5
Answer: A

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