Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
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) Register the model in the Azure Machine Learning workspace.
B) Tag the training experiment with a name.
C) Export the model files to local storage.
D) Locate and capture the model artifacts from the outputs of the training run.

2. You train models on GPU-enabled clusters but deploy them on CPU-based endpoints. Recently, inference failures occur due to incompatible dependencies. What should you do to ensure consistency?

A) Increase endpoint compute size
B) Use same compute for training and inference
C) Use batch endpoints
D) Define and reuse environment configurations

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

4. A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
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) Assign a resource-level Azure AI Administrator role to the platform engineers.
B) Disable Microsoft Entra ID authentication for the Microsoft Foundry resource.
C) Assign the Azure AI Developer role to the developers.
D) Share a single API key across all teams.

5. A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?

A) Increase timeout settings
B) Set minimum instance count
C) Reduce logging
D) Switch to batch endpoint

Solutions: