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Microsoft AI-300 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Optimize generative AI systems and model performance | 15–20% | - Improve efficiency and cost-effectiveness
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:
You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image.
A new version of the Docker image is available.
You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2.
What should you do?
- A. Use the Environment class to create a new version of the environment.
- B. Use the create_or_update method to change the tag of the image.
- C. Change the description parameter of the environment configuration.
- D. Modify the conda_file to specify the new version of the Docker image.
Correct Answer: A 🗳️
Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?
- A. mlflow.log_image()
- B. mlflow.log_artifact()
- C. mlflow.log_batch()
- D. mlflow.log_metric()
Correct Answer: C 🗳️
Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a data asset in the workspace.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
You need to recommend an experiment-tracking strategy that ensures consistent experiment results.
What should you recommend?
- A. Application Insights logs
- B. Azure Monitor alerts
- C. MLflow experiment tracking
- D. Azure Machine Learning job output logs
Correct Answer: C 🗳️
Explanation: Only visible for Pass4sures members. You can sign-up / login (it's free).
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
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