Exam Name: | Designing and Implementing a Data Science Solution on Azure | ||
Exam Code: | DP-100 Dumps | ||
Vendor: | Microsoft | Certification: | Microsoft Azure |
Questions: | 460 Q&A's | Shared By: | daniella |
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
You create machine learning models by using Azure Machine Learning.
You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.
You need to select the appropriate compute types.
Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type 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.
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
• /data/2018/Q1 .csv
• /data/2018/Q2.csv
• /data/2018/Q3.csv
• /data/2018/Q4.csv
• /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:
Solution: Run the following code:
Does the solution meet the goal?