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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. 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) Deploy the model to a batch endpoint.
C) Replace the existing endpoint.
D) Route all traffic to the new deployment.

2. Hotspot Question
You use Azure Machine Learning to train models across multiple experiments by using the same workspace.
You must record training runs in a centralized location to compare results from different jobs.
During training, performance values must be captured so they appear in the experiment run history.
You need to configure experiment tracking.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

3. An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
- Model behavior aligns with the task being performed.
- Data handling aligns with internal governance policies.
- Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. Choose three.
NOTE: Each correct selection is worth one point.

A) A model that offers enterprise governance controls when workloads process regulated business data
B) A model that supports multiple input types when workloads require combined text and image analysis
C) A model that is optimized for conversational reasoning when deploying an interactive assistant
D) The largest available model to simplify operational management
E) The smallest available model to minimize the usage cost

4. Drag and Drop Question
A team iterates prompts used by a generative AI agent. The team must support internal review before releasing changes.
The team must:
- Track prompt changes with a clear history for audit and rollback.
- Compare prompt variants in parallel without affecting the prompt used in the production environment.
You need to select the appropriate source control approach for each requirement.
What should you use for each requirement? To answer, move the appropriate source controls to the correct requirements. You may use each source control 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.

5. You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data for multi-turn chat.
Which file encoding method should you use?

A) ISO-8859-1
B) UTF-16
C) UTF-8
D) ASCII

Solutions: