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Exam DP-100 topic 1 question 12 discussion

Actual exam question from Microsoft's DP-100
Question #: 12
Topic #: 1
[All DP-100 Questions]

You construct a machine learning experiment via Azure Machine Learning Studio.
You would like to split data into two separate datasets.
Which of the following actions should you take?

  • A. You should make use of the Split Data module.
  • B. You should make use of the Group Categorical Values module.
  • C. You should make use of the Clip Values module.
  • D. You should make use of the Group Data into Bins module.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️

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dp100uber
Highly Voted 3 years, 5 months ago
Selected Answer: A
Split data is correct, not D.
upvoted 34 times
Gabonia
2 years, 9 months ago
I agree
upvoted 1 times
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lianaliam
Most Recent 1 day, 17 hours ago
Selected Answer: A
test_split to two part
upvoted 1 times
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PopeyeDS
8 months, 2 weeks ago
Selected Answer: A
The Split Data module is specifically designed for dividing a dataset into multiple parts. It allows you to specify the ratio or proportion of data to allocate to each resulting dataset. By configuring the Split Data module, you can split your data into two separate datasets based on your desired split ratio, such as 70% for training and 30% for testing.
upvoted 2 times
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evangelist
8 months, 2 weeks ago
Selected Answer: A
A. You should make use of the Split Data module. The Split Data module in Azure Machine Learning Studio is specifically designed for dividing datasets into two or more parts based on specified criteria. This is useful for creating training and testing datasets, which is a common requirement in machine learning experiments to evaluate the performance of models. The other modules mentioned serve different purposes unrelated to splitting datasets for training and testing.
upvoted 2 times
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eternaleclipse
1 year, 7 months ago
A. This material is not updated.
upvoted 1 times
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james2033
1 year, 7 months ago
Selected Answer: A
Split Data module.
upvoted 1 times
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PradhanManva
1 year, 8 months ago
Selected Answer: A
This is the answer.
upvoted 1 times
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endeesa
1 year, 12 months ago
Split data module is the correct answer
upvoted 1 times
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frogman_8654
2 years ago
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-data?view=azureml-api-2
upvoted 2 times
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musuai
2 years, 1 month ago
Know-it-all Bot: A. You should make use of the Split Data module. Me: Why not D? Know-it-all Bot: The Group Data into Bins module is used to group continuous data into discrete bins or intervals. It is not appropriate for splitting data into separate datasets. In contrast, the Split Data module is specifically designed for splitting data into training and testing datasets based on a specified percentage or other criteria, and would be the appropriate choice in this scenario.
upvoted 1 times
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Truman
2 years, 2 months ago
Selected Answer: A
To split data into two separate datasets in Azure Machine Learning Studio, you can use the "Split Data" module, which can be found in the "Data Transformation" category in the module palette.
upvoted 2 times
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varunhebbar
2 years, 2 months ago
Selected Answer: A
Option A
upvoted 1 times
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Ammy_b
2 years, 3 months ago
Answer : A - Split the Data Module will able to split the dataset into two like training and testing
upvoted 1 times
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Mirjalol
2 years, 4 months ago
D is for 'old' Machine Learning studio, now Azure ML studio has Split option
upvoted 2 times
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ZIMARAKI
2 years, 4 months ago
Selected Answer: A
A is the correct answer
upvoted 1 times
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jais7508
2 years, 7 months ago
Selected Answer: A
https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/split-data A is the answer , the ans selected as B is selected based on old Machine Learning Studio but now azure machine learning is used to perform any task and according to that A is right. https://learn.microsoft.com/en-us/previous-versions/azure/machine-learning/studio-module-reference/cross-validate-model
upvoted 1 times
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JTWang
2 years, 8 months ago
Selected Answer: A
A is the answer!
upvoted 2 times
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