Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
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You manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py --trainingdata ${{inputs.training_data}}
Does the solution meet the goal?

• A. No
• B. Yes

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You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?

• A. mlflow.log_image()
• B. mlflow.log_artifact()
• C. mlflow.log_metric()
• D. mlflow.log_batch()

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You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?

• A. fairlearn.preprocessing.CorrelationRemover
• B. fairlearn.postprocessing.ThresholdOptimizer
• C. fairiearn.reductions.ExponentiatedGradient
• D. fairlearn.reductions.GridSearch

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Hotspot Question
You have an Azure Machine Learning workspace named Workspace1.
You plan to train an image classification model by using Automated ML in Workspace1.
You need to complete the provided Azure Machine Learning Python SDK v2 code to bring labeled image data as input for model training.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:
Box 1: azure.ai.ml.constants
azure.ai.ml.constants is the official SDK v2 submodule where the AssetType enum resides.
Box 2: MLTABLE
AssetType.MLTABLE: Automated ML for Computer Vision tasks (such as image classification and object detection) specifically requires your input data and corresponding label annotations to be provided via an MLTable asset type. This structure points to a folder containing your dataset configurations and your .jsonl bounding box or classification files.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models

You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible AI dashboard to assess the model.
You need to use the Azure Machine Learning studio UI to generate the Responsible AI dashboard.
What should you do first?

• A. Convert the model from the MLflow format to a custom format.
• B. Deploy the model to a managed online endpoint.
• C. Create the model explanations.
• D. Register the model with the workspace.

Explanation: Only visible for ExamcollectionPass members. You can sign-up / login (it's free).