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| Vendor: | Microsoft |
|---|---|
| Exam Code: | DP-100 |
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Questions: | 506 |
| Last Updated: | October 25, 2025 |
| Related Certifications: | Azure Data Scientist Associate |
| Exam Tags: | Microsoft Azure certifications, Cloud certifications Intermediate Microsoft Data Scientists and machine learning professionals |
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You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.
You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.
Which parameter and value pair should you use in the AutoMLConfig class?
Featurization str or FeaturizationConfig
Values: 'auto' / 'off' / FeaturizationConfig
Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.
Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:
Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.
Numeric: Impute missing values, cluster distance, weight of evidence.
DateTime: Several features such as day, seconds, minutes, hours etc.
Text: Bag of words, pre-trained Word embedding, text target encoding.
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?
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
You create an Azure Machine Learning workspace.
You must configure an event handler to send an email notification when data drift is detected in the workspace datasets. You must minimize development efforts.
You need to configure an Azure service to send the notification.
Which Azure service should you use?
You have a Python script that executes a pipeline. The script includes the following code:
from azureml.core import Experiment
pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)
You want to test the pipeline before deploying the script.
You need to display the pipeline run details written to the STDOUT output when the pipeline completes.
Which code segment should you add to the test script?
wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.
Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True)
Parameter: show_output
Indicates whether to show the run output on sys.stdout.
You need to select an environment that will meet the business and data requirements.
Which environment should you use?
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