What does the DP-100 syllabus cover?

The official DP-100 syllabus covers the domains and topics that Microsoft lists in the current exam guide for Designing and Implementing a Data Science Solution on Azure.

Where can I verify the latest DP-100 skills measured?

Use the official source links on this page to verify the latest DP-100 skills measured, topic areas, and outline updates before exam day.

Microsoft Exam Syllabus

DP-100 syllabus, skills measured, and exam topics

The DP-100 exam measures Design and prepare a machine learning solution, Explore data, and run experiments, and Train and deploy models. Use this page to review the current official syllabus, major domains, and source links before exam day.

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Domain	Weight
Design and prepare a machine learning solution	20–25%
Explore data, and run experiments	20–25%
Train and deploy models	25–30%
Optimize language models for AI applications	25–30%

Purpose of this document

This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
Useful links: Description
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Take a free Practice Assessment: Test your skills with practice questions to help you prepare for the exam.

Updates to the exam

We always update the English language version of the exam first. Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized versions as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.

Audience profile

As a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI.
Your responsibilities for this role include:
Designing and creating a suitable working environment for data science workloads.
Exploring data.
Training machine learning models.
Implementing pipelines.
Running jobs to prepare for production.
Managing, deploying, and monitoring scalable machine learning solutions.
Using language models for building AI applications.
As a candidate for this exam, you should have knowledge and experience in data science by using:
Azure Machine Learning
MLflow

Design and prepare a machine learning solution (20–25%)

Identify the structure and format for datasets
Determine the compute specifications for machine learning workload
Select the development approach to train a model
Create and manage a workspace
Create and manage datastores
Create and manage compute targets
Set up Git integration for source control
Create and manage data assets
Create and manage environments
Share assets across workspaces by using registries

Explore data, and run experiments (20–25%)

Use automated machine learning for tabular data
Use automated machine learning for computer vision
Use automated machine learning for natural language processing
Select and understand training options, including preprocessing and algorithms
Evaluate an automated machine learning run, including responsible AI guidelines
Use the terminal to configure a compute instance
Access and wrangle data in notebooks
Wrangle data interactively with attached Synapse Spark pools and serverless Spark compute
Retrieve features from a feature store to train a model
Track model training by using MLflow
Evaluate a model, including responsible AI guidelines
Select a sampling method

Train and deploy models (25–30%)

Consume data in a job
Configure compute for a job run
Configure an environment for a job run
Track model training with MLflow in a job run
Define parameters for a job
Run a script as a job
Use logs to troubleshoot job run errors
Create custom components
Create a pipeline
Pass data between steps in a pipeline
Run and schedule a pipeline
Monitor and troubleshoot pipeline runs

Optimize language models for AI applications (25–30%)

Select and deploy a language model from the model catalog
Compare language models using benchmarks
Test a deployed language model in the playground
Select an optimization approach
Test prompts with manual evaluation
Define and track prompt variants
Create prompt templates
Define chaining logic with the prompt flow SDK
Use tracing to evaluate your flow
Prepare data for RAG, including cleaning, chunking, and embedding
Configure a vector store
Configure an Azure AI Search-based index store