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

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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1
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You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2- based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Question #2
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?

A. mltable.from_parquet_files()
B. mltable.load()
C. mltable.from_delimited_files()
D. mltable.from_delta_lake()

Question #3
You have an Azure Machine Learning workspace named workspaces.
You must add a datastore that connects an Azure Blob storage container to workspaces. You must be able to configure a privilege level.
You need to configure authentication.
Which authentication method should you use?

A. Managed identity
B. Account key
C. SAS token
D. Service principal

Question #4
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 job class.
B. Configure the tracking URI.
C. Log into workspace " !.
D. Instantiate the MLCIient class.

Question #5
A machine learning model is deployed to production in Azure Machine Learning and is actively serving predictions for a business application. The model was trained by using a historical dataset that represented expected input patterns at the time of deployment.
The team working on the model must ensure the following:
Changes in input data distribution are detected.
Appropriate actions are triggered when predefined thresholds are exceeded.
You need to configure monitoring to meet the requirements.
Which configuration should you use for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Solutions: