Free Microsoft DP-100 Exam Questions

Become Microsoft Certified with updated DP-100 exam questions and correct answers

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Total 511 Questions | Updated On: Jan 06, 2026

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Question 1

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?

A : Yes

B : No

Answer: B

Question 2

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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference

to the experiment run context, loads data from a file, identifies the set of unique values for the label column,

and completes the experiment run:

from azureml.core import Run

import pandas as pd

run = Run.get_context()

data = pd.read_csv('data.csv')

label_vals = data['label'].unique()

# Add code to record metrics here

run.complete()

The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

You must add code to the script to record the unique label values as run metrics at the point indicated by the

comment.

Solution: Replace the comment with the following code:

for label_val in label_vals:

run.log('Label Values', label_val)

Does the solution meet the goal?

A :

B :

Answer: A

Question 3

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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method. Does the solution meet the goal?

A : Yes

B : No

Answer: A

Question 4

You are a data scientist creating a linear regression model.

You need to determine how closely the data fits the regression line.

Which metric should you review?

A : efficient of determination

B : call

C : ecision

D : an absolute error

E : ot Mean Square Error

Answer: A

Question 5

You have a dataset that contains records of patients tested for diabetes. The dataset includes the patient's age.

You plan to create an analysis that will report the mean age value from the differentially private data derived from the dataset.

You need to identify the epsilon value to use in the analysis that minimizes the risk of exposing the actual data.

Which epsilon value should you use?

A : -1.5

B : -0.5

C : 0.5

D : 1.5

Answer: C

Page: 1 / 103

Total 511 Questions | Updated On: Jan 06, 2026

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