Free Microsoft DP-100 Exam Questions

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

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Total 525 Questions | Updated On: Jun 30, 2025

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Question 1

You are profiling mltabte data assets by using Azure Machine Learning studio. You need to detect columns with odd or missing values. Which statistic should you analyze?

A : ofile

B : deviation

C : ror count

D : pe

Answer: C

Question 2

You have an Azure Machine Learning workspace.

You plan to tune a model hyperparameter when you train the model.

You need to define a search space that returns a normally distributed value.

Which parameter should you use?

A : QUniform

B : LogUniform

C : Uniform

D : LogNormal

Answer: D

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 a data scientist using Azure Machine Learning Studio.

You need to normalize values to produce an output column into bins to predict a target column.

Solution: Apply an Equal Width with Custom Start and Stop binning mode.

Does the solution meet the goal?

A :

B :

Answer: B

Question 4

You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has

missing values in many columns. The data does not require the application of predictors for each column. You

plan to use the Clean Missing Data module to handle the missing data.

You need to select a data cleaning method.

Which method should you use?

A : nthetic Minority Oversampling Technique (SMOTE)

B : place using MICE

C : place using; Probabilistic PCA

D : rmalization

Answer: C

Question 5

You use the Azure Machine Learning SDK to run a training experiment that trains a classification model and

calculates its accuracy metric.

The model will be retrained each month as new data is available.

You must register the model for use in a batch inference pipeline.

You need to register the model and ensure that the models created by subsequent retraining experiments are

registered only if their accuracy is higher than the currently registered model.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

A : Specify a different name for the model each time you register it.

B : Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline

C : Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

D : Specify a property named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy property value of the currently registered model.

E : Specify a tag named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.

Answer: D,E

Page: 1 / 105

Total 525 Questions | Updated On: Jun 30, 2025

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