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Last Updated: Aug 14, 2026
No. of Questions: 528 Questions & Answers with Testing Engine
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| Books / Training | DP-100T01-A: Designing and Implementing a Data Science Solution on Azure |
| Exam Price | $165 (USD) |
| Number of Questions | 40-60 |
| Sample Questions | Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions |
| Duration | 120 mins |
| Exam Code | DP-100 |
| Schedule Exam | Pearson VUE |
| Exam Name | Microsoft Certified - Azure Data Scientist Associate |
| Passing Score | 700 / 1000 |
The kind of extensive subject matter for the Microsoft DP-100 can be only obtained once the aspirant has access to quality study resources that are in general endorsed by the vendor itself. Overall, Microsoft offers both free and paid training courses to gain demanded expertise for DP-100. When it comes to the gratis learning path, it contains the following self-paced classes:
As far as you can see, all these are the best options to consider when free learning resources for the Microsoft DP-100 exam are concerned. Still, there is one paid instructor-led training course as well. It covers the concepts related to Designing & Implementing a Data Science Solution on Azure and is known as Course DP-100T01-A, which is a 3-day long class explaining every bit of the exam domains. In particular, the course imparts in applicants the intermediate knowledge of ML solutions, Python to monitor data preparation & ingestion, and ML frameworks. During the whole training, you’ll get exposed to as many as 10 comprehensive modules that spin around the test objectives. What makes these lessons significant is that they contain labs in the curriculum unlike free self-paced learning, which allows you to develop practical skills that are generally necessary for job-oriented tasks. So, visit the vendor’s website, check whether you comply with the course prerequisites and if you do, enroll in it, and be entirely geared up for the Microsoft DP-100 exam.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explore data and run experiments | 20-25% | - Explore and visualize data
|
| Topic 2: Optimize language models for AI applications | 25-30% | - Implement generative AI solutions
|
| Topic 3: Train and deploy models | 25-30% | - Train models
|
| Topic 4: Design and prepare a machine learning solution | 20-25% | - Manage data assets
|
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