HOTSPOT

You create an Azure Machine Learning workspace

You are developing a Python SDK v2 notebook to perform custom model training in the workspace.

The notebook code imports all required packages.

You need to complete the Python SDK v2 code to include a training script. environment, and compute information.

How should you complete ten code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point

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You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.

You develop a training script for the model on a local machine.

You need to load the model fairness metrics into Azure Machine Learning studio.

What should you do?

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You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.

You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.

You need to modify the inference pipeline to meet the requirement.

What should you do?

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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.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You use Azure Machine Learning designer to load the following datasets into an experiment:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Add Rows module.

Does the solution meet the goal?

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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.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv(‘data.csv’)

label_vals = data[‘label’].unique()

# Add code to record metrics here run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

Solution: Replace the comment with the following code:

for label_val in label_vals:

run.log(‘Label Values’, label_val)

Does the solution meet the goal?

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You need to select a feature extraction method.

Which method should you use?

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HOTSPOT

You are running a training experiment on remote compute in Azure Machine Learning.

The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.

You must use MLflow as the logging package for tracking metrics generated in the experiment.

You need to complete the script for the experiment.

How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

Box 1: import mlflow

Import the mlflow and Workspace classes to access MLflow’s tracking URI and configure your workspace.

Box 2: mlflow.start_run()

Set the MLflow experiment name with set_experiment() and start your training run with start_run().

Box 3: mlflow.log_metric(‘ ..’)

Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.

Box 4: mlflow.end_run()

Close the run:

run.endRun()

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

HOTSPOT

You have an Azure Machine Learning workspace

You plan to use the Azure Machine Learning SDK for Python v1 to submit a job to run a training script.

You need to complete the script to ensure that it will execute the training script.

How should you complete the script? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point

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HOTSPOT

You create a new Azure Machine Learning workspace with a compute cluster.

You need to create the compute cluster asynchronously by using the Azure Machine Learning Python SDK v2.

How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point

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HOTSPOT

You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.

The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.

You need to evaluate the outcome of the early termination policy

What should you evaluate? To answer, select the appropriate options m the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer: