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Updated: Oct 14, 2026
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Microsoft DP-100 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Certificate Validity Period: | 1 year |
| Exam Price: | $165 USD |
| Exam Format: | Case studies, Yes/No, Drag and drop, Multiple choice, Multiple select |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Azure AI Engineer Associate |
| Available Languages: | German, Italian, Arabic (Saudi Arabia), Chinese (Simplified), Korean, French, Russian, English, Portuguese (Brazil), Indonesian (Indonesia), Japanese, Spanish, Chinese (Traditional) |
| Passing Score: | 700 |
| Exam Duration: | 100 minutes |
| Real Exam Qty: | 40-60 |
| Recommended Training: | Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure Microsoft Learn Learning Path |
| Exam Registration: | Microsoft Learn Registration Pearson VUE Scheduling |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 |
The Microsoft DP-100 test helps candidates check their machine learning knowledge and skills and prove themselves as qualified data scientists.
Step 1: Reviewing the Exam Topics
The best way to start the preparation process is to visit the official website of the certification exam. The official site is the most reliable source for the right information regarding the test. It is recommended that you download a detailed list of the exam objectives from the webpage and thoroughly study it before attempting the actual test.
The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:
1. Azure ML Workspace Set-Up (30-35%):
- Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
- Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
- Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.
How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Optimize language models for AI applications | 25-30% | - Optimize with Retrieval Augmented Generation
|
| Train and deploy models | 25-30% | - Deploy models
|
| Explore data and run experiments | 20-25% | - Explore and visualize data
|
| Design and prepare a machine learning solution | 20-25% | - Manage data assets
|
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
You create an Azure Machine Learning workspace and install the MLflow library.
You need to tog different types of data by using the MLflow library.
Which method should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?
- A. workspacefi lest ore datastore
- B. workspaceblobstore datastore
- C. compute instance
Correct Answer: C 🗳️
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Correct Answer:
Explanation:
You manage an Azure Machine Learning workspace. You plan to import data from Azure Data Lake Storage Gen2. You need to build a URI that represents the storage location. Which protocol should you use?
- A. abfss
- B. wasbs
- C. adl
- D. https
Correct Answer: A 🗳️
You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.
The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.
You need to configure compute targets for model testing and production.
Which compute targets should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Local web service
The Local web service compute target is used for testing/debugging. Use it for limited testing and troubleshooting. Hardware acceleration depends on use of libraries in the local system.
Box 2: Azure Kubernetes Service (AKS)
Azure Kubernetes Service (AKS) is used for Real-time inference.
Recommended for production workloads.
Use it for high-scale production deployments. Provides fast response time and autoscaling of the deployed service Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
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