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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. An organization maintains separate Azure Machine Learning workspaces for development and production.
Both environments must use the same validated assets without duplicating them.
Assets must be shared across workspaces while maintaining centralized governance and version control.
You need to enable reuse of assets across workspaces without copying them.
What should you do?

A) Create a shared Azure Machine Learning environment that includes the asset.
B) Publish the asset to an Azure Machine Learning registry.
C) Publish the asset as a pipeline component.
D) Enable workspace-level Git integration and sync assets between repositories.

2. 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) Disable public network access to the Microsoft Foundry resource.
B) Deploy the Microsoft Foundry resource in a separate Azure subscription.
C) Configure a managed virtual network for the Microsoft Foundry resource.
D) Disable all inbound network access.
E) Use API key authentication for all model endpoints.

3. An Azure Machine Learning workspace processes sensitive training data.
The workspace must NOT be accessible from the public internet.
You need to restrict network access.
Which configuration should you implement?

A) Azure Firewall rules
B) Service endpoints
C) Network security groups
D) Private endpoints

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

5. You deploy a model to production but do not have labeled data available for evaluating prediction accuracy. However, you must monitor model health continuously. What is the BEST strategy?

A) Use data drift detection
B) Disable monitoring
C) Monitor accuracy metrics
D) Perform manual evaluation only

Solutions: