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

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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1
You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.
You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.
You need to continue to log metrics and artifacts from your data science code.
What should you do?

A. Instantiate the MLCIient class.
B. Instantiate the job class.
C. Configure the tracking URI.
D. Log into workspace " !.

Question #2
You have an Azure Machine Learning workspace.
You have the following code:

You plan to rely on serverless compute to train a model by using Azure Machine Learning Python SDK v2.
The serverless compute must use a designated number of nodes of a specific virtual machine type.
You need to modify the code to run the training job according to the plan.
How should you modify the command object? To answer, select the appropriate oations in the answer area.
NOTE: Each correct selection is worth one point.

Question #3
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named modeljrain.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question #4
You have an Azure Machine Learning workspace that includes an AmICompute cluster and a batch endpoint.
You clone a repository that contains an MLflow model to your local computer. You need to ensure that you can deploy the model to the batch endpoint.
Solution: Create a data asset in the workspace.
Does the solution meet the goal?

A. Yes
B. No

Question #5
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. NOTE: Each correct selection is worth one point. Choose two.

A. Disable Microsoft Entra ID authentication for the Microsoft Foundry resource.
B. Share a single API key across all teams.
C. Assign a resource-level Azure AI Administrator role to the platform engineers.
D. Assign the Azure AI Developer role to the developers.

Solutions: