Certification guideDP-100

Microsoft Azure Data Scientist Associate: the honest guide

Everything Microsoft Azure publishes about DP-100, in one place: what the exam asks, how the domains are weighted, and what it takes to be ready.

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Everything below comes from Azure Data Scientist’s published exam data, and every figure links to the vendor page it came from. The researched version, with study plans and the parts nobody publishes, is still being written. This page is not submitted to search engines until it is.

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What the exam actually asks you to do

Multiple choice and multiple response, plus drag-and-drop ordering, hot area items where you click a region of a screenshot, and case studies that hold one scenario across several linked questions.

  • Multiple choice
  • Multiple response
  • Drag and drop
  • Hot area
  • Case study

The highlighted formats are the ones you cannot answer from memory alone. Microsoft Learn, exam duration and question types

Domain breakdown and official weightings

From the official Microsoft Learn study guides. Train and deploy models is the heaviest domain at 28 percent, followed by Optimize language models for AI applications at 27 percent.

  • Design and prepare a machine learning solution23%
  • Explore data, and run experiments22%
  • Train and deploy models28%
  • Optimize language models for AI applications27%

Microsoft Azure: Microsoft Learn study guides

What comes after passing

Role-based certifications last one year and renew free through an online assessment on Microsoft Learn. The only cost is the hour it takes.

Costs across the full renewal cycle are on the Azure Data Scientist cost page.

Frequently asked questions

How has DP-100 shifted recently?

A quarter of the exam is now optimizing language models for AI applications: prompt flows, fine-tuning choices and retrieval augmented generation in Azure AI Foundry, alongside the classic Azure Machine Learning workspace material.

Is DP-100 about data science theory?

No, it is about doing data science on Azure: workspaces, compute targets, jobs and pipelines, MLflow tracking and managed endpoints. It assumes you already know what a model is and tests where the platform fits in.

Note on domain weightings

Microsoft publishes weightings as ranges. The figures here use midpoints of the official ranges from the current DP-100 study guide.

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Every guide and cost breakdown, by vendor

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