Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
- Predictions must not disproportionately impact protected groups.
- Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Measure endpoint latency under load.
B) Evaluate feature importance for prediction transparency.
C) Analyze error rates across defined demographic cohorts.
D) Validate inference schema compatibility.
E) Analyze error rates across the global cohort.

2. Drag and Drop Question
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.

3. Hotspot Question
A team trains an MLflow model that scores customer churn risk. The model will be consumed by different downstream systems.
One system requests predictions synchronously during customer interactions.
Another system submits files containing millions of records for scheduled scoring.
You need to deploy the model by using managed inference options that match each usage pattern.
Which option should you use for each usage pattern? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. Drag and Drop Question
A team deploys a machine learning model to a managed online endpoint. The team monitors model performance and data quality metrics in production.
When monitoring thresholds are exceeded, the team requires an automated operational response that notifies downstream systems.
You need to configure the monitoring solution to meet the requirements.
Which configuration should you associate with each requirement as a first step? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

5. Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1. The container stores 10 blobs with files in the CSV format.
You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.
You need to complete the Python SDK v2 code.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Solutions: