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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?

A) Yes
B) No

2. Multiple teams need access to approved models with version tracking, lineage, and governance controls. Models must be discoverable and reusable across projects. What Azure ML feature should you use?

A) Data lake
B) Git repositories
C) Model registry
D) Blob storage containers

3. A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A) GitHub pull request checks
B) Azure Monitor alerts
C) Conditional logic in pipeline
D) Manual approval gate

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

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

Solutions: