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

You manage an Azure Machine Learning workspace. The Pylhon scrip! named scriptpy reads an argument named training_data. The trainlng.data argument specifies the path to the training data in a file named datasetl.csv.

You plan to run the scriptpy Python script as a command job that trains a machine learning model.

You need to provide the command to pass the path for the datasct as a parameter value when you submit the script as a training job.

Solution: python script.py –training_data dataset1,csv

Does the solution meet the goal?

You manage are Azure Machine Learning workspace by using the Python SDK v2.

You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an auto scaling compute target and a data asset for the job.

You need to configure the resources for the job.

Which resource configuration should you use? to answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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 Apply Transformation module.

Does the solution meet the goal?

You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count

You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?

You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma-separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:

You have the following code:

You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?

A)

B)

C)

D)

You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.

You must predict the fare of a taxi trip.

You need to select performance metrics to correctly evaluate the- regression model.

Which two metrics can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

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 are analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Remove the entire column that contains the missing data point.

Does the solution meet the goal?

You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.

You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.

Which code segment should you run?