Drag and Drop Question
An organization is deploying generative AI solutions by using Microsoft Foundry to support multiple production workloads.
The organization has the following workload requirements:
- One workload must be real-time, latency-sensitive, and have
predictable global usage patterns that demand consistent performance.
- One workload must have variable performance and be optimized for
cost-efficient operation.
You need to select a global deployment type for each workload.
Which type of deployment should you use for each workload requirement? To answer, move the appropriate deployment types to the correct requirements. You may use each deployment type 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.

Correct Answer:

Comment: *

Name: *

Email: *

Verification: *

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

Comment: *

Name: *

Email: *

Verification: *

An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?

• A.Archive the training dataset for the model version.
• B.Delete the model version.
• C.Archive the model version.
• D.Unregister the model version.

Comment: *

Name: *

Email: *

Verification: *

Your ML pipeline contains independent feature engineering steps that currently execute sequentially, increasing overall runtime. You want to optimize execution without modifying logic.
What is the BEST solution?

• A.Combine all steps
• B.Enable parallel step execution
• C.Increase compute size
• D.Reduce dataset size

Comment: *

Name: *

Email: *

Verification: *

An organization validates generative AI applications during CI/CD Microsoft Foundry.
Evaluation must run automatically and block releases when quality thresholds are NOT met. Manual evaluation is no longer acceptable.
Evaluation must use both predefined quality metrics and custom safety checks.
You need to implement an automated evaluation workflow that supports both built-in and custom metrics .
What should you do?

• A.Enable application tracing to collect runtime telemetry.
• B.Review evaluation results manually after deployment.
• C.Monitor latency metrics during model inference.
• D.Implement an evaluation step by using GitHub Actions.

Comment: *

Name: *

Email: *

Verification: *