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DP-100 Pack
Vendor: Microsoft
Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Exam Questions: 506
Last Updated: July 11, 2026
Related Certifications:
Exam Tags: Intermediate Microsoft Data Scientists and machine learning professionals
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Free Microsoft DP-100 Exam Actual Questions

Question No. 1

You are evaluating a completed binary classification machine learning model.

You need to use the precision as the valuation metric.

Which visualization should you use?

Show Answer Hide Answer
Correct Answer: A

https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/

Question No. 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 create a model to forecast weather conditions based on historical data.

You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

Solution: Run the following code:

Does the solution meet the goal?

Show Answer Hide Answer
Correct Answer: B

Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azureml.pipeline.core import Pipeline, PipelineData

from azureml.pipeline.steps import PythonScriptStep

datastore = ws.get_default_datastore()

process_step_output = PipelineData('processed_data', datastore=datastore)

process_step = PythonScriptStep(script_name='process.py',

arguments=['--data_for_train', process_step_output],

outputs=[process_step_output],

compute_target=aml_compute,

source_directory=process_directory)

train_step = PythonScriptStep(script_name='train.py',

arguments=['--data_for_train', process_step_output],

inputs=[process_step_output],

compute_target=aml_compute,

source_directory=train_directory)

pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])


https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py

Question No. 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 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,f2i

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?

Show Answer Hide Answer
Correct Answer: B

Use two file paths.

Use Dataset.Tabular_from_delimeted, instead of Dataset.File.from_files as the data isn't cleansed.


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets

Question No. 4

You create an Azure Machine Learning workspace.

You must configure an event-driven workflow to automatically trigger upon completion of training runs in the workspace. The solution must minimize the administrative effort to configure the trigger.

You need to configure an Azure service to automatically trigger the workflow.

Which Azure service should you use?

Show Answer Hide Answer
Correct Answer: A

Question No. 5

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.

Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?

Show Answer Hide Answer
Correct Answer: E

Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network.


https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-sdk

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