Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

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

2. Hotspot Question
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. 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

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) Register the data as a tabular dataset in the Azure Machine Learning workspace.
B) Configure an Azure Machine Learning compute cluster for model training.
C) Schedule training using an Azure Data Factory pipeline.
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. Hotspot Question
You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts.
You need to configure data asset properties.
Which values should you use in your configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: