Microsoft AI-300 : Operationalizing Machine Learning and Generative AI Solutions

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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) Locate and capture the model artifacts from the outputs of the training run.
C) Export the model files to local storage.
D) Tag the training experiment with a name.

2. You must ensure full reproducibility of experiments including dataset, code, and environment across multiple runs and workspaces. Which combination of practices is MOST appropriate?

A) Logging metrics only
B) Dataset versioning only
C) Environment + dataset + code versioning
D) Git only

3. 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.
Which file format should you use?

A) CSV
B) JSONL
C) JSON
D) TSV

4. Drag and Drop Question
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action 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. Drag and Drop Question
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration 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.

Solutions: