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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?

A) Adjust hyperparameters after model deployment.
B) Create a tuning job that runs multiple trials with different parameter values.
C) Duplicate the training script for each parameter combination.
D) Manually change hyperparameter values between training runs.

2. 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?

A) Archive the model version.
B) Archive the training dataset for the model version.
C) Unregister the model version.
D) Delete the model version.

3. A team accidentally deploys an outdated model version due to incorrect tagging. You need to enforce strict deployment governance and version control. What should you implement?

A) Rename models
B) Increase logging
C) Use version pinning and approval workflows
D) Store models locally

4. You are designing a new machine learning solution to predict customer churn by using Azure Machine Learning. You have raw data in CSV format stored in Azure Data Lake.
You need to design the solution so that it can efficiently handle large-scale model training and iterative development.
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) Schedule training using an Azure Data Factory pipeline.
B) Register the data as a tabular dataset in the Azure Machine Learning workspace.
C) Configure an Azure Machine Learning compute cluster for model training.
D) Convert the data into the JSONL format and upload into Blob Storage.
E) Configure an Azure Machine Learning compute instance for model training.

5. Drag and Drop Question
You manage an Microsoft Foundry project.
You deploy a large language model from the model catalog.
You need to manually evaluate the model, collect the statistics, and be able to review the results later.
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.

Solutions: