REALISTIC NEW DP-100 TEST CAMP - DESIGNING AND IMPLEMENTING A DATA SCIENCE SOLUTION ON AZURE LATEST DUMPS SHEET PASS GUARANTEED QUIZ

Realistic New DP-100 Test Camp - Designing and Implementing a Data Science Solution on Azure Latest Dumps Sheet Pass Guaranteed Quiz

Realistic New DP-100 Test Camp - Designing and Implementing a Data Science Solution on Azure Latest Dumps Sheet Pass Guaranteed Quiz

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Microsoft DP-100 certification exam is designed for data scientists and data engineers who want to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam is intended for individuals who have experience using Azure to build and deploy machine learning models, as well as those who have experience with data storage and processing on Azure.

To prepare for the DP-100 exam, candidates can take instructor-led training courses, self-paced online courses, and practice tests. Microsoft offers several training courses that cover the exam objectives, including "Designing and Implementing a Data Science Solution on Azure" and "Implementing an Azure Data Solution." Candidates can also access online resources such as Microsoft documentation and GitHub repositories to learn about Azure technologies and data science concepts.

What is the duration of the DP-100 Exam

  • Length of Examination: 150 minutes
  • Passing Score: 700/1000
  • Number of Questions: 45-60
  • Format: Multiple choices, multiple answers

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q497-Q502):

NEW QUESTION # 497
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values


NEW QUESTION # 498
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 creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than tin- other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Principal Components Analysis (PCA) sampling mode.
Does the solution meet the goal?

  • A. No
  • B. Yes

Answer: A

Explanation:
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning.
SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.


NEW QUESTION # 499
You create a new Azure subscription. No resources are provisioned in the subscription.
You need to create an Azure Machine Learning workspace.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/ workspaces resource and its dependencies.
  • B. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying -w and -g parameters for the workspace name and resource group.
  • C. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.
  • D. Navigate to Azure Machine Learning studio and create a workspace.
  • E. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.

Answer: A,B,D

Explanation:
B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning.
Example:
{"type": "Microsoft.MachineLearningServices/workspaces",
...
C: You can create a workspace for Azure Machine Learning with Azure CLI Install the machine learning extension.
Create a resource group: az group create --name <resource-group-name> --location <location> To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w <workspace-name> -g <resource-group-name> D: You can create and manage Azure Machine Learning workspaces in the Azure portal.
Sign in to the Azure portal by using the credentials for your Azure subscription.
In the upper-left corner of Azure portal, select + Create a resource.
Use the search bar to find Machine Learning.
Select Machine Learning.
In the Machine Learning pane, select Create to begin.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace


NEW QUESTION # 500
Hotspot Question
You create an Azure Machine Learning workspace.
You need to detect data drift between a baseline dataset and a subsequent target dataset by using the DataDriftDetector class.
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.

Answer:

Explanation:

Explanation:
Box 1: create_from_datasets
The create_from_datasets method creates a new DataDriftDetector object from a baseline tabular dataset and a target time series dataset.
Box 2: backfill
The backfill method runs a backfill job over a given specified start and end date.
Syntax: backfill(start_date, end_date, compute_target=None, create_compute_target=False) Incorrect Answers:
List and update do not have datetime parameters.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-
datadrift/azureml.datadrift.datadriftdetector(class)


NEW QUESTION # 501

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 502
......

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