Microsoft AI-300

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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question #1
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.

Question #2
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Question #3
You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target an environment and a training script You need to prepare information to submit a training job. Which class should you use?

A. MLClient
B. EndpointConncction
C. command
D. BuildContext

Question #4
You train a model in Azure Machine Learning.
You plan to capture experiment details for later comparison. The training code must log parameters and metrics for each run.
You review the following training script.

You need to verify whether the training script meets the experiment tracking requirement.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Question #5
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A team performs interactive experimentation during development. The team also runs scalable jobs for model training.
The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.
You need to configure compute targets that support each workload.
Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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: