CertLabProMicrosoft Azure Data Scientist Associate
225 अभ्यास प्रश्न
अंतिम समीक्षा: April 2026
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DP-100 validates the day-to-day skills of a data scientist working on Azure: designing ML solutions, exploring and preparing data, training and deploying models in Azure Machine Learning, and — since the 2024 refresh — optimizing language models for AI applications. The audience is practicing data scientists and ML engineers who write Python against the Azure ML SDK / CLI v2 and use Azure ML studio. The exam is heavier on Azure-specific implementation than on classical statistics or algorithm theory: expect 40–60 questions in 100 minutes including code-completion drag-and-drops, scenario items, and at least one case study.
About 22%. Choosing compute and storage for ML workloads, Azure ML workspaces, datastores and data assets, environments, and responsible-AI considerations at design time.
About 22%. Azure ML notebooks, AutoML for classification / regression / forecasting / NLP / CV, Azure ML designer, and basic MLflow integration for experiment tracking.
Largest classical-ML domain at 28%. Training jobs (script and command jobs), distributed training, hyperparameter sweep jobs, model registration, managed online endpoints, batch endpoints, and pipelines.
New domain added in 2024 at 28% weight. Prompt flow, fine-tuning foundation models in Azure ML / Azure AI Foundry, evaluating LLM applications, RAG patterns, and responsible-AI controls for generative scenarios.
$115k–$165k–$230k USD annual
Range covers US-based mid-to-senior data scientists where Azure ML proficiency is required. FAANG / unicorn applied scientists often clear $300k TC. Cert is a screening signal; demonstrated modeling experience and publication / kaggle / open-source presence drive the high end.
Source: levels.fyi 2025 data scientist / ML engineer roles, U.S. BLS OEWS May 2024 (15-2051 data scientists, 15-2099 ML scientists), Glassdoor 2025. Figures are approximate; actual compensation depends on role, region, and experience.
DP-100 has held steady demand as enterprises operationalize ML on Azure ML and increasingly on Azure AI Foundry. Recruiters treat it as the canonical Azure ML proof point — most useful for data scientists who need to demonstrate they can ship beyond a notebook into managed endpoints and pipelines. The 2024 LLM-optimization domain has made DP-100 more attractive to GenAI engineers as well. It pairs naturally with AI-102 for engineers building production GenAI apps and with DP-203 / DP-700 for data-engineer-leaning ML practitioners.
There are no formal prerequisites, but DP-100 assumes practitioner-level data-science skills going in. Microsoft's outline expects fluency in Python, the scikit-learn / pandas / NumPy stack, and the core ML workflow (split, train, evaluate, deploy). DP-900 is a useful conceptual on-ramp for candidates new to Azure data services, but is not required.
The official Microsoft Learn path covers all four domains in roughly 30–40 hours, focused on Azure ML SDK / CLI v2 and prompt flow. Hands-on time is essentially required: a personal Azure subscription with a small Azure ML workspace, plus 10+ hours running real training jobs, model deployments, and prompt-flow runs. The 2024 LLM-optimization domain is under-covered by older third-party material, so candidates should rely on Microsoft Learn modules for that area.
DP-100 sits in the Associate tier and is generally considered moderately difficult — easier than AZ-204 / AI-102 for experienced data scientists, harder for engineers new to ML. Plan on 60–100 hours of study over 6–10 weeks with prior data-science experience; substantially longer if Python ML is new to you. The exam runs about 100 minutes with 40–60 questions in multiple-choice, multiple-response, drag-and-drop (including code-completion), hot-area, and case-study formats.
The most common stumbling block is Azure ML SDK / CLI v2 specifics — Microsoft's recent migration from SDK v1 to v2 broke many third-party study guides, so older material may show outdated YAML and command shapes. The new LLM-optimization domain (prompt flow, fine-tuning, evaluation) has a learning curve of its own and tends to surprise candidates who treated DP-100 as a classical-ML exam.
Major refresh adding the LLM-optimization domain (28% weight), modernizing training-job and deployment material to Azure ML SDK / CLI v2, and integrating Azure AI Foundry concepts. Microsoft refreshes DP-100 approximately every 12–18 months without changing the exam code.
Migrated from Azure ML SDK v1 to SDK / CLI v2 framing, retired Azure ML designer-heavy questions, and added MLflow integration coverage.
Initial GA, replacing the retired DP-100 (legacy code). Original outline focused on Azure ML designer, AutoML, and SDK v1.
DP-100 (Microsoft Azure Data Scientist Associate) is a a moderately difficult exam expecting practical hands-on experience plus solid understanding of best practices Associate-level exam. Most candidates need 80–150 hours of study spread over 6–12 weeks for associate-level exams. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.