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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Implement machine learning model lifecycle and operations | 25–30% | - Monitor and maintain models in production
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
A team runs training jobs by using multiple Azure Machine Learning pipelines.
The team must ensure that all runs use the same Python packages and system libraries. The solution must allow dependency updates to be versioned without modifying training code.
You need to configure the workspace so that runtime dependencies are consistent and reusable.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
To ensure runtime dependencies are consistent and reusable, first create a conda.yaml or requirements.txt file that lists all Python packages and system libraries required by your training code - this file is the single source of truth for your runtime. Next, create an Environment object using the Azure ML Python SDK v2 with a name and reference to the conda.yaml file, specifying the base Docker image. Then register the Environment by calling ml_client.environments.create_or_update, which publishes it to the workspace registry with an auto-incremented version. Finally, reference the registered environment by name and version in all pipeline job steps. Azure ML will build or retrieve the cached Docker image and use it as the execution container. This approach means updating dependencies only requires modifying the conda.yaml and registering a new version - training code remains unchanged.
Microsoft Learn Reference Topic: Create and manage Azure Machine Learning environments - Reusable curated environments
You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.
What should you do first?
- A. Create a compute cluster.
- B. Create an experiment.
- C. Create an attached compute resource.
- D. Select a model.
Correct Answer: C 🗳️
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupiter notebook in the workspace. The experiment must log string metrics.
You need to implement the method to log the string metrics.
Which method should you use?
- A. mlflow.log-metric0
- B. mlflow.log-text0
- C. mlflow.log. dist0
- D. mlflow.log. artifact0
Correct Answer: B 🗳️
You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?
- A. Run a simulator to produce telemetry logs and trace data from user interactions.
- B. Export model evaluation logs and use them directly as training data.
- C. Enable A/B testing and capture live user traffic for data generation.
- D. Use a simulator to generate prompt-response interaction data that matches the target task.
Correct Answer: D 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics.
What should you do?
- A. Implement an evaluation step by using GitHub Actions.
- B. Review evaluation results manually after deployment.
- C. Monitor latency metrics during model inference.
- D. Enable application tracing to collect runtime telemetry.
Correct Answer: A 🗳️
Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).
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