Microsoft DP-100 real exam prep : Designing and Implementing a Data Science Solution on Azure

Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Updated: Nov 04, 2026
Q&As: 528 Questions and Answers

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About Microsoft DP-100 Real Exam

Obligatory Prerequisites

Officially, there is no prior work-experience or educational expertise related to DP-100 exam. Anyone, willing to make it big in the world of data science, can go for it. However, industry pundits say that beginner-level expertise in concepts like running data experiments and machine learning will make the exam journey a lot more simplified and easy to accomplish.

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3. Models Management and Optimization (20-25%):

Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.
Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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Microsoft DP-100 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Design and prepare a machine learning solution	20-25%	- Design a machine learning solution 1. Determine dataset structure and format 2. Define compute specifications for workloads 3. Select development approach 4. Plan model deployment requirements - Manage compute resources 1. Create and configure compute targets 2. Attach and monitor compute 3. Select environments - Manage data assets 1. Select storage services 2. Create and maintain data assets 3. Register and manage datastores - Manage Azure Machine Learning workspace 1. Work with registries 2. Create and configure workspace 3. Use developer tools and CLI 4. Set up Git integration
Topic 2: Optimize language models for AI applications	25-30%	- Implement generative AI solutions 1. Build prompt flows 2. Use Azure AI Foundry 3. Apply prompt engineering - Optimize with Retrieval Augmented Generation 1. Prepare and process data 2. Create vector stores and indexes 3. Configure Azure AI Search - Evaluate and improve models 1. Test and evaluate responses 2. Apply responsible generative AI 3. Optimize for accuracy and safety
Topic 3: Explore data and run experiments	20-25%	- Implement pipelines 1. Pass data between steps 2. Schedule and monitor pipelines 3. Create and publish pipelines 4. Build reusable components - Run experiments 1. Track runs with MLflow 2. Configure experiment runs 3. Use automated machine learning 4. Define parameters and configurations - Explore and visualize data 1. Detect anomalies and outliers 2. Identify features and relationships 3. Profile and validate data
Topic 4: Train and deploy models	25-30%	- Deploy models 1. Deploy to online endpoints 2. Configure compute and scaling 3. Secure endpoints and manage access 4. Deploy to batch endpoints - Train models 1. Use HyperDrive for hyperparameter tuning 2. Configure jobs and environments 3. Run training scripts 4. Apply responsible AI principles - Manage models 1. Package and validate models 2. Register and version models 3. Interpret models and explain predictions - Monitor and maintain models 1. Implement MLOps practices 2. Update and retrain models 3. Monitor performance and data drift

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:

Question #1

You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.
You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Box 1: No
FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.
Box 2: Yes
to_path() gets a list of file paths for each file stream defined by the dataset.
Box 3: Yes
TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.
TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset

Question #2

You manage an Azure Machine Learning workspace.
You must set up an event-driven process to trigger a retraining pipeline.
You need to configure an Azure service that will trigger a retraining pipeline in response to data drift in Azure Machine Learning datasets. Which Azure service should you use?

A. Logic Apps
B. Event Grid
C. Azure Functions
D. Event Hubs

Reveal Solution Discussion 0

Correct Answer: B 🗳️

Question #3

You have an Azure Machine Learning workspace.
You run the following code in a Python environment in which the configuration file for your workspace has been downloaded.

instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Question #4

You have an Azure Machine Learning workspace. You are connecting an Azure Data Lake Storage Gen2 account to the workspace as a data store. You need to authorize access from the workspace to the Azure Data Lake Storage Gen2 account.
What should you use?

A. Managed identity
B. SAS token
C. Account key
D. Service principal

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Question #5

You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit- learn framework to train a machine learning model.
You must run the script as an Azure Machine Learning experiment on your local workstation.
You need to write Python code to initiate an experiment that runs the train.py script.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Reveal Solution Discussion 0

Correct Answer:

Explanation:

Box 1: source_directory
source_directory: A local directory containing code files needed for a run.
Box 2: script
Script: The file path relative to the source_directory of the script to be run.
Box 3: environment
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig

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Microsoft DP-100 real exam prep : Designing and Implementing a Data Science Solution on Azure

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