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Microsoft DP-100 Exam (page: 3)
Microsoft Designing and Implementing a Data Science Solution on Azure
Updated on: 12-Feb-2026

Viewing Page 3 of 102
DP-100 PDF 504 Questions

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.
You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.
Recommendation: You configure the use of the value k=10.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): A

Explanation:

Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.
LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.
This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.



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?

  1. You should make use of the Split Data module.
  2. You should make use of the Group Categorical Values module.
  3. You should make use of the Clip Values module.
  4. You should make use of the Group Data into Bins module.

Answer(s): A

Explanation:

The Group Data into Bins module supports multiple options for binning data. You can customize how the bin edges are set and how values are apportioned into the bins.


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins



You have been tasked with creating a new Azure pipeline via the Machine Learning designer.
You have to makes sure that the pipeline trains a model using data in a comma-separated values (CSV) file that is published on a website. A dataset for the file for this file does not exist.
Data from the CSV file must be ingested into the designer pipeline with the least amount of administrative effort as possible.
Which of the following actions should you take?

  1. You should make use of the Convert to TXT module.
  2. You should add the Copy Data object to the pipeline.
  3. You should add the Import Data object to the pipeline.
  4. You should add the Dataset object to the pipeline.

Answer(s): C

Explanation:

The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web
URL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.
Example:
from azureml.core import Dataset
iris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')])


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.
You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.
Recommendation: You make use of the Replace with median option.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): A

Explanation:


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data



This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.
You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.
Recommendation: You make use of the Custom substitution value option.
Will the requirements be satisfied?

  1. Yes
  2. No

Answer(s): A

Explanation:


Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data



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