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Azure Data Scientist Associate

by Microsoft AI Team · Microsoft Learn

4.4
(3,500 reviews)
150K+ enrolled8 hoursUpdated 2025-01
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Our Verdict

Consider alternatives

Important status change as of mid-2026: Microsoft retired the DP-100 exam and the Azure Data Scientist Associate certification on June 1, 2026, and the official certification page now states 'No training available for this exam' and that 'this certification and the renewal assessment are retired.' So while the underlying Microsoft Learn modules on Azure Machine Learning, MLflow, and Azure AI remain useful free study material, you can no longer sit the exam or earn this specific credential. The skills it covered are still current and valuable (the syllabus was modernized in 2025 to devote 25-30% to optimizing language models via prompt flow, RAG, and fine-tuning), but Microsoft is steering learners toward newer AI-era credentials such as the Machine Learning Operations Engineer Associate (AI-300) path. We rate this as an independent editorial analysis of the official syllabus plus aggregated public learner feedback (4.4/5 from 80 reviews on the Coursera-hosted version), not a personal course completion.

The exam and certification were officially retired on June 1, 2026, and Microsoft now shows 'No training available for this exam.' You cannot earn the credential anymore, so pursuing it as a certification goal is no longer viable. The free Microsoft Learn content is still worth reading for Azure ML skills, but anyone wanting a current, exam-backed Azure AI/ML certification should pivot to Microsoft's newer AI-era path (e.g., AI-300 MLOps Engineer Associate) rather than this one.

Best for: Practitioners who already use Azure Machine Learning and want free, vendor-authored reference material on AML workspaces, MLflow tracking, pipelines, AutoML, online/batch deployment, and responsible AI. It also suits people who simply want to understand the modern Azure data-science stack (including the 2025 additions on prompt flow, RAG, and fine-tuning with Azure AI Foundry, now Microsoft Foundry) without needing a certificate.

Skip if: Anyone whose goal is to actually earn the DP-100 credential or pass the exam, since both were retired on June 1, 2026. It is also not ideal for complete beginners: the syllabus assumes working Python plus prior machine-learning knowledge (Scikit-Learn, PyTorch, TensorFlow), and it is Azure-specific, so it teaches a cloud platform more than ML fundamentals.

About This Course

Prepare for DP-100 certification covering Azure ML workspace, automated ML, pipelines, and responsible AI on Azure.

What You'll Learn

Design and prepare an Azure Machine Learning solution: manage workspaces, datastores, compute targets, data assets, and environments (20-25% of the former exam)
Explore data and run experiments using Automated ML (tabular, computer vision, NLP) and notebooks, with MLflow experiment tracking and hyperparameter tuning (20-25%)
Train and deploy models: run training scripts as jobs, build training pipelines with custom components, register MLflow models, and deploy to online and batch endpoints (25-30%)
Optimize language models for AI applications: prompt engineering with prompt flow, Retrieval Augmented Generation (RAG) with vector and Azure AI Search index stores, and model fine-tuning (25-30%)
Apply Responsible AI principles when evaluating AutoML runs, custom models, and registered models
Use MLflow to track, package, and manage the model lifecycle on Azure

Curriculum

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

Design an ML solution and choose compute/dataset structure; create and manage an Azure Machine Learning workspace, datastores, compute targets, and Git source control; create and manage data assets, environments, and shared registries.

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

Use Automated ML for tabular data, computer vision, and NLP; use notebooks for custom training, wrangle data, retrieve features from a feature store, and track training with MLflow; automate hyperparameter tuning (sampling method, search space, primary metric, early termination).

Train and deploy models (25-30%)

Run model training scripts as jobs (consume data, configure compute/environment, log with MLflow, troubleshoot via logs); build training pipelines with custom components; register and manage MLflow models; deploy to online endpoints and batch endpoints and invoke batch scoring.

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

Select and deploy a language model from the catalog and benchmark it; optimize via prompt engineering and prompt flow (variants, templates, chaining, tracing); implement RAG (cleaning, chunking, embedding, vector store, Azure AI Search index, evaluation); and fine-tune a base model with prepared data.

Prerequisites

  • Working Python programming ability
  • Prior machine-learning knowledge and experience with frameworks such as Scikit-Learn, PyTorch, or TensorFlow
  • An Azure subscription / account to run the hands-on Azure Machine Learning exercises
  • Familiarity with core data-science workflows (data exploration, model training, evaluation)

Instructor

Microsoft AI Team

Instructor · Microsoft Learn

Pros & Cons

Pros

  • Free, vendor-authored content directly from Microsoft Learn that maps to the real Azure Machine Learning stack used in production
  • Modern, up-to-date syllabus (refreshed in 2025): a full 25-30% covers current generative-AI skills like prompt flow, RAG, and fine-tuning, not just classic ML
  • Strong hands-on orientation around AML v2, MLflow tracking, pipelines, and online/batch deployment rather than pure theory
  • Solid learner sentiment on the equivalent course (4.4/5 from 80 reviews), with reviewers noting it genuinely prepared them for the workload

Cons

  • The exam and certification were retired on June 1, 2026, so the credential can no longer be earned and 'No training available for this exam' is shown on the official page
  • Reviewers report broken GitHub dataset links in some Python SDK lab exercises, requiring workarounds
  • Azure-specific and intermediate: it assumes solid Python and prior ML knowledge and teaches a cloud platform, so it is a poor fit for ML beginners or non-Azure users
  • Platform churn: Azure AI Foundry was rebranded to Microsoft Foundry and Microsoft notes materials are being updated, so some references may be stale or inconsistent

Alternatives To Consider

Azure AI Fundamentals

Microsoft Learn

View course

Machine Learning Scientist with Python

DataCamp

View course

Deep Learning Specialization

Coursera

View course

Frequently Asked Questions

Is Azure Data Scientist Associate free?

Yes — Azure Data Scientist Associate is free to access. The Microsoft Learn self-paced modules are free. Earning the credential previously required passing exam DP-100 (US $165; 700/1000 to pass), but both the exam and certification were retired on June 1, 2026, so that paid path no longer exists. A separate Coursera-hosted version of this content is available on subscription but is not required.

Who is Azure Data Scientist Associate for?

Practitioners who already use Azure Machine Learning and want free, vendor-authored reference material on AML workspaces, MLflow tracking, pipelines, AutoML, online/batch deployment, and responsible AI. It also suits people who simply want to understand the modern Azure data-science stack (including the 2025 additions on prompt flow, RAG, and fine-tuning with Azure AI Foundry, now Microsoft Foundry) without needing a certificate.

What will you learn in Azure Data Scientist Associate?

Design and prepare an Azure Machine Learning solution: manage workspaces, datastores, compute targets, data assets, and environments (20-25% of the former exam); Explore data and run experiments using Automated ML (tabular, computer vision, NLP) and notebooks, with MLflow experiment tracking and hyperparameter tuning (20-25%); Train and deploy models: run training scripts as jobs, build training pipelines with custom components, register MLflow models, and deploy to online and batch endpoints (25-30%); Optimize language models for AI applications: prompt engineering with prompt flow, Retrieval Augmented Generation (RAG) with vector and Azure AI Search index stores, and model fine-tuning (25-30%).

What are the prerequisites for Azure Data Scientist Associate?

Working Python programming ability; Prior machine-learning knowledge and experience with frameworks such as Scikit-Learn, PyTorch, or TensorFlow; An Azure subscription / account to run the hands-on Azure Machine Learning exercises; Familiarity with core data-science workflows (data exploration, model training, evaluation).

Is Azure Data Scientist Associate worth it?

The exam and certification were officially retired on June 1, 2026, and Microsoft now shows 'No training available for this exam.' You cannot earn the credential anymore, so pursuing it as a certification goal is no longer viable. The free Microsoft Learn content is still worth reading for Azure ML skills, but anyone wanting a current, exam-backed Azure AI/ML certification should pivot to Microsoft's newer AI-era path (e.g., AI-300 MLOps Engineer Associate) rather than this one.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Microsoft Learn's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

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