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

2. A team plans to deploy a large foundation model in Microsoft Foundry as part of a new enterprise AI capability.
Different business units across the team's organization will access the model from various internal applications.
You need to deploy a foundation model by minimizing latency.
Which deployment type should you use?

A) Data Zone Standard
B) Developer
C) Global Batch
D) Data Zone Batch

3. 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) Add a data validation component in pipeline
D) Retrain manually when failure occurs

4. You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?

A) Manual testing workflow
B) Separate endpoint for testing
C) Batch endpoint validation
D) Traffic splitting with monitoring alerts

5. A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
The team needs to introduce a new version of a model to production without disrupting existing users.
The team must validate the new version before full rollout.
You need to reduce risk during deployment.
What should you do?

A) Route all traffic to the new deployment.
B) Split traffic between deployments.
C) Deploy the model to a batch endpoint.
D) Replace the existing endpoint.

Solutions: