Microsoft Certified: Azure Data Scientist Associate

Azure's data science credential, covering training and running ML models with Azure Machine Learning.

What it signals

Practical assessment: Scenario-based multiple choice. Proctored, scenario-based; labs not confirmed.
Identity assurance: Proctored with identity verification. Was proctored via Pearson VUE; can no longer be taken.
Depth: Associate. Associate level.
Experience expected: About 1 year. Expected working data science experience; no year count stated.
Portability: Mixed. MLflow and general ML practice transfer; workspace specifics are Azure ML.
Verifiability: Public badge or registry. Credential is shareable via a Microsoft Learn verification link/transcript carrying an 'Online Verifiable' tag that an employer can open (profile must be public).

For hiring managers

Microsoft no longer offers a data-scientist certification on Azure; the nearest current credential (AI-300) is an operations role.
All DP-100 certifications lapse by about June 2027 because renewal is no longer possible.
Versions before January 2025 had no language-model content.

Verifying it

Candidate shares a link from their Microsoft Learn profile: either a per-credential Share link (credentials carry an 'Online Verifiable' tag) or a transcript share link / PDF. The Learn profile must be in public view for the shared credential to be verifiable. Retired credentials remain shareable and stay on the transcript.

What it covers

Published exam outline

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%

By topic

GenAI apps: Substantial (20 to 35%). 'Optimize language models for AI applications' (prompt flow, RAG, fine-tuning) was 25-30%.
Agents: Not covered. No agent content.
Classical ML: Core focus (over 35%). Experimentation, AutoML, hyperparameter tuning and training jobs spanned two domains, over 35%.
Operations: Substantial (20 to 35%). Pipelines, model registration and online/batch deployment made up most of a 25-30% domain, roughly 20%.
Data: Some (10 to 20%). Data assets, datastores, wrangling and feature store were roughly 10-15%.
Responsible AI: Touched on (under 10%). Only a few 'responsible AI guidelines' evaluation bullets.

Exam details

Who the provider says it is for. Data scientists implementing and running ML workloads on Azure.

Format: Microsoft does not publish per-exam formats. Official question types across the programme: multiple choice, drag and drop, build list, hot area, active screen, case studies, and (on some role-based exams) labs. Exam page states: 'This exam will be proctored. You may have interactive components to complete as part of this exam.'
Delivery: Was proctored via Pearson VUE; can no longer be taken.
Code or hands-on work: Objectives were Python SDK/notebook/MLflow centric, so reading code was expected. Presence of labs not confirmed.
Prerequisites: None
Recommended experience: Subject matter expertise in data science and ML on Azure using Azure Machine Learning, MLflow, Azure AI services/AI Search and Azure AI Foundry.
Renewal: NOT renewable any more: the renewal assessment was retired with the certification.
Price notes: USD figure is from third-party sources; Microsoft prices vary by country.

Preparing

DP-100 study guide (official, archived)

Not confirmed

These details were not available from an official source at the time of research.

priceUsd (official pages only say price depends on country; USD figure is from third-party sources)
questionCount (not published per exam)
exact question formats for this specific exam (Microsoft does not disclose)
online-vs-test-centre delivery options were not confirmed on this exam's own page
durationMinutes (not shown on the retired page)
official wording of what replaces DP-100

Sources (9)