What skills are expected?

Solid data science background (Python, notebooks, supervised/unsupervised models), experience with Azure ML (workspace, compute, registries, pipelines), and MLOps basics.

What’s the exam format?

Multiple-choice and scenario questions covering design, training, deployment, and monitoring. Duration and scoring can change—check the official page.

How to prepare effectively?

Follow a progressive plan with hands-on labs in Azure ML: experiments, AutoML, pipelines, deployment, monitoring, and Responsible AI; validate with mock tests.

DP-100 Exam Guide: Azure Data Scientist Associate

Q: Who should take DP-100?

Data scientists and ML engineers who design, train, and deploy models with Azure Machine Learning, focusing on MLOps and responsible AI.

Scope, measured skills, exam format, resources, and a study plan to design, train, and deploy models with Azure Machine Learning.

Measured skills 2–4 week study plan Go to DP-100 course

Who it’s for & prerequisites

Ideal profiles

Data scientists and ML engineers working in cloud environments.
Teams industrializing ML with pipelines and MLOps.
Professionals coming from DP-900 targeting production.

Prerequisites

Python, notebooks, and foundational data science.
Azure cloud concepts; experience with Azure ML is a plus.
Recommended baseline: AI-900 / DP-900.

Measured skills (overview)

Setup & data

Workspace, compute, datastores/datasets; data preparation and versioning.

Training & AutoML

Experiments, runs, tracking; AutoML, hyperparameters, metrics, and model registry.

Pipelines, deploy & MLOps

Orchestrated pipelines, deployment to endpoints/AKS, monitoring, drift, Responsible AI.

Helpful comparisons

AI-900 vs DP-100 — AI fundamentals vs data science on Azure
AI-102 vs DP-100 — AI engineering vs data science

Exam format & booking

Format

Multiple-choice and scenario-based questions on Azure ML.
Indicative duration: ~90–120 minutes (variable).
Languages: confirm on the official booking page.

Note: details and scoring may change—always verify the official page.

Booking

Microsoft account; choose online or test center.
Valid ID and compliant environment (for online proctoring).

Prepare with the DP-100 course

Study plan (2–4 weeks)

2 weeks (intensive)

Days 1–3: workspace/compute setup and data management.
Days 4–7: experiments & AutoML; model registration.
Days 8–10: pipelines and deployment.
Days 11–14: monitoring, Responsible AI, mock tests.

4 weeks (standard)

Week 1: data & environments.
Week 2: training & tuning.
Week 3: pipelines & deploy.
Week 4: MLOps, monitoring, simulations.

Open the detailed plan Read the FAQs

Recommended resources

Azure Machine Learning

Hands-on guidance on experiments, pipelines, and deployment.

Open the guide

FAQs & Glossary

Clear definitions and answers on Azure AI concepts and services.

FAQs · Glossary

Labs & simulators

Exercises and mock tests to validate your readiness.

Open simulator

Ready to take models to production?

Join the official course and certify as Azure Data Scientist Associate.

Enroll in DP-100 Back to Azure AI hub

FAQ

Who should take DP-100?

Data scientists and ML engineers aiming to operate end‑to‑end with Azure ML.

What prerequisites are recommended?

Python, ML basics, Azure experience; AI-900/DP-900 are helpful.

How long and what’s the structure?

About 90–120 minutes with multiple-choice/scenario questions; always check the official page for current details.

How should I plan my study?

Follow the 2–4 week plan with hands-on labs, pipelines, and monitoring.