DP-100 Exam Dumps - Designing and Implementing a Data Science Solution on Azure

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.

You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.

There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.

You need to exclude the 20 files. What should you do?

You have an Azure Machine Learning workspace.

You plan to use the workspace to set up automated machine learning training for an image classification model.

You need to choose the primary metric to optimize the model training.

Which primary metric should you choose?

You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription.

You must manage the Synapse Spark pool from the Azure Machine Learning workspace.

You need to attach the Synapse Spark pool in Azure Machine Learning by usinq the Python SDK v2.

Which three actions should you perform in sequence? To answer move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

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:

run.log_table('Label Values', label_vals)

Does the solution meet the goal?

You have several machine learning models registered in an Azure Machine Learning workspace.

You must use the Fairlearn dashboard to assess fairness in a selected model.

Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary

classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

Which two hyperparameters should you use? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You need to implement a feature engineering strategy for the crowd sentiment local models.

What should you do?