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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?

A) Same dataset name
B) Same compute SKU
C) Same user identity
D) Versioned datasets and environments

2. Drag and Drop Question
A team operates a generative AI-powered customer support assistant built on Microsoft Foundry.
The application serves users globally and supports both real-time chat interactions and batch summarization jobs.
The team must ensure that the application continues to meet defined service-level objectives (SLO) as usage increases.
The team requires visibility into runtime behavior to identify performance regressions that affect the user experience and system capacity.
You need to select the performance metrics that meet the requirements.
Which performance metric should you monitor for each requirement? To answer, move the appropriate performance metrics to the correct requirements. You may use each performance metric 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.

3. Drag and Drop Question
A team deploys a machine learning model to production and monitors it continuously. Alerts are configured on performance and data quality metrics.
Multiple alerts are triggered during normal operation.
You need to perform the appropriate action for each model alert condition.
Which action should you perform for each alert condition? To answer, move the appropriate actions to the correct model alert conditions. You may use each action 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.

4. During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?

A) Use larger compute
B) Ignore schema differences
C) Retrain manually when failure occurs
D) Add a data validation component in pipeline

5. A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
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) Use API key authentication for all model endpoints.
B) Disable public network access to the Microsoft Foundry resource.
C) Disable all inbound network access.
D) Deploy the Microsoft Foundry resource in a separate Azure subscription.
E) Configure a managed virtual network for the Microsoft Foundry resource.

Solutions: