Azure Mastery
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Azure Mastery
Microsoft Certification DP-100
On-device AI scores your readiness, builds an adaptive study plan, and flags topics fading from memory — before they cost you the exam.
Review the final DP-100 outline on iPhone or iPad, preserve the Azure Machine Learning skills you learned, and compare them with current Microsoft credential options. Core study stays on-device and works offline; optional sync uses your private iCloud account.
The exam
DP-100 was the exam for Microsoft Certified: Azure Data Scientist Associate. Microsoft retired both the exam and certification on 1 June 2026, so DP-100 can no longer be scheduled. AI-300 is a current adjacent option for operationalising machine learning and generative AI solutions, but it is not a continuation of the retired credential under the same name. This page and question bank remain available for reference.
DP-100 is hands-on and code-aware. It validates that you can use the Azure Machine Learning workspace end-to-end — designing data assets and compute, exploring data with notebooks and AutoML, training models with MLflow tracking and Sweep jobs for hyperparameter tuning, deploying to managed online and batch endpoints, and now optimising language models with prompt engineering, fine-tuning, and RAG patterns. Expect scenario questions that show you a YAML pipeline spec or Python SDK snippet and ask you to predict behaviour — not just describe an algorithm.
Microsoft last updated the DP-100 skills outline on 11 April 2025, adding the "Optimize language models for AI applications" domain at 25–30%. Every question in Azure Mastery's DP-100 bank is mapped to that final outline. Read the archived official outline at learn.microsoft.com.
Skills measured · April 2025
Four domains, with weights set by Microsoft's April 2025 update. Every domain summary below is paraphrased from the official skills outline; bullet-level objectives in Azure Mastery are tagged so you always know which domain you're being tested on and where your weak spots cluster.
The setup phase. Covers Azure Machine Learning workspace design — assets (data, environments, models), compute targets (compute instances, compute clusters, attached compute, serverless), and identity (managed identities, role assignments). Plus data assets (URI, MLTable), datastore registration (Blob, ADLS Gen2, key-based vs identity-based access), and curated vs custom environments. Around 8–15 questions per sitting.
Notebook-driven exploration in compute instances or Visual Studio Code, plus the Designer low-code surface and Automated ML for the no-code path. Covers MLflow integration for experiment tracking and metric logging, hyperparameter tuning with Sweep jobs (sampling strategies, early termination policies, primary metric), and choosing between AutoML and a manual pipeline. Around 8–15 questions.
Tied for the largest domain. Job configuration — command jobs, pipeline jobs, parallel jobs, run context, output handling. Model registration (MLflow vs custom format) and versioning. Deployment to managed online endpoints (real-time, blue/green and traffic split), batch endpoints, and Kubernetes-attached compute. Inference monitoring, data drift detection, and retraining pipelines. Around 10–18 questions.
New in the April 2025 outline, tied for the largest domain. Covers prompt engineering basics (system prompts, few-shot examples, chain-of-thought), fine-tuning workflows on Azure Machine Learning and Azure AI Foundry, retrieval-augmented generation (RAG) with embeddings and Azure AI Search, evaluating LLM outputs (groundedness, relevance, fluency, similarity), and responsible-AI considerations specific to generative AI. Around 10–18 questions.
Designed for DP-100
Azure Mastery ships with 350 DP-100 practice questions, every one written specifically against the current (April 2025) skills outline. Each question carries a domain tag mapped to the official four domains (design/prepare ML, explore/experiment, train/deploy, optimize LLMs), so you always know which area you're being tested on and where your weak spots are clustered. YAML pipeline specs, Python SDK snippets, and Azure ML configuration scenarios appear throughout — matching the format of the live exam.
The on-device Exam IQ engine can still estimate your performance against the final DP-100 bank for reference. After roughly 30 questions it identifies the specific retired-outline topics that need review, helping you retain Azure Machine Learning knowledge while comparing current credentials.
The adaptive study plan rebuilds itself from your answer history. Get a Sweep job sampling-strategy question wrong? You'll see another hyperparameter-tuning scenario in the next session. Master "managed online endpoint vs batch endpoint" three sessions running and the engine backs off, surfacing fresh prompt-engineering or RAG scenarios. The plan optimises for the gap between where you are and the 700 pass score, not for blind volume.
Knowledge decay tracking matters more for DP-100 than for foundational exams — four ML domains span a lot of code-aware surface area, and the topic you mastered three weeks into your study window is the topic you'll forget by exam day if you stop revising. Azure Mastery tracks every topic's decay curve and flags topics approaching expiry. The padlock icon on the Today screen is your "revisit before you forget" cue, and weak-spot drills automatically pull from decayed topics first.
Historical simulation mode can still run a 40–60-question set from the full 350-question bank with the former 100-minute timing. It is retained for reference and does not represent an exam that can still be booked.
Answer Coach turns each missed answer into a private, grounded lesson: the misconception, key distinction, and rule to remember. It always uses authored certification guidance; on supported devices, an optional on-device model may rewrite the note only when it passes grounding checks.
During your first week, Aura adapts the next step as you go. Every session ends with a concise recap of what changed, what to focus on, and the best follow-up.
Everything essential runs on-device. Your answer history, readiness gauge, and coaching stay private. Optional sync uses your private iCloud account; there is no Azure Mastery account, tracking, or external processing server.
6-week study plan
Most candidates pass DP-100 after four to eight weeks of focused study, depending on prior Python and ML experience. The six-week plan below maps onto the four DP-100 domains, Azure Mastery's adaptive sessions, and the in-app exam simulator. Adjust pace to taste — the readiness gauge tells you when you're done, not the calendar.
Inside the app
DP-100's question bank uses the same formats Microsoft puts on the live exam — not just multiple choice. Each visualisation below is a faithful mock of how the type renders inside Azure Mastery on iPhone and iPad. Exam-simulator mode runs all of them at full 100-minute length with no flag-and-review jumps, mirroring Pearson VUE.
A real DP-100 question-bank example with one correct answer. The app explains every option after you answer.
Exam-specific sampleA real DP-100 multi-select item. Every required selection must be correct to earn the mark.
All-or-nothingA real DP-100 interactive-format prompt, rendered for touch on iPhone and iPad.
Interactive itemA real DP-100 prompt that tests recognition inside a visual or contextual interface.
Tap targetA real DP-100 case-study scenario with linked questions that share the same requirements and environment.
Multi-questionAnswer Coach uses the bank's authored rationale to explain the misconception, key distinction, and rule to remember. On supported devices, an optional on-device model may rewrite the note only when it passes grounding checks.
App exclusiveFrequently asked
No. Microsoft retired DP-100 and the Azure Data Scientist Associate certification on 1 June 2026. The exam can no longer be scheduled.
Microsoft did not continue the retired Azure Data Scientist Associate credential under the same name. AI-300 is a current adjacent option focused on operationalising machine learning and generative AI solutions, and Microsoft lists AI-300 courseware as the successor to DP-100 courseware.
A certification earned before retirement remains on your Microsoft Learn transcript. DP-100 and its renewal assessment are retired, so the exam cannot be retaken or newly earned.
Yes, as reference material for Azure Machine Learning, training, deployment, MLflow, responsible AI, and language-model optimisation. New candidates should compare those skills with current AI-300 and other active Microsoft credentials before choosing a path.
AI-300 is Microsoft's replacement route for operationalizing machine learning and generative AI. AI-103 covers developing AI apps and agents with Python and Microsoft Foundry. Neither recreates the retired DP-100 data-scientist credential exactly, so choose based on whether your work centres on AI operations or application development.
Where DP-100 fits
DP-100 and the Azure Data Scientist Associate certification retired on 1 June 2026. Microsoft identifies AI-300 as the replacement credential, with a changed emphasis on machine-learning and generative-AI operations.
Keep 350 final-outline DP-100 questions for reference, or use Azure Mastery's current AI-300 bank and adaptive study plan for a current machine-learning operations option.
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