DA0-001 vs DA0-002: What Changed?

The basic exam format remains familiar

Both official objective documents describe multiple-choice and performance-based questions and a 90-minute test. DA0-001 lists 90 questions, while DA0-002 states a maximum of 90 questions. Both recommend practical exposure to databases, analytical tools, statistics, and data visualization.

The shared format does not make the content interchangeable. A study resource can still teach useful fundamentals while failing to cover the objective language and emphasis of the exam you will take.

The domain names and weights changed

DA0-001 assigned 15% to Data Concepts and Environments, 25% to Data Mining, 23% to Data Analysis, 23% to Visualization, and 14% to Data Governance, Quality, and Controls.

DA0-002 assigns 20% to Data Concepts and Environments, 22% to Data Acquisition and Preparation, 24% to Data Analysis, 20% to Visualization and Reporting, and 14% to Data Governance. The newer map increases the share for concepts, slightly increases analysis, and reframes the other domain titles.

• Domain 1 rises from 15% to 20%.
• Data Mining becomes Data Acquisition and Preparation and moves from 25% to 22%.
• Data Analysis moves from 23% to 24%.
• Visualization becomes Visualization and Reporting and moves from 23% to 20%.
• The governance domain remains 14%, with a shorter title and revised objectives.

DA0-002 makes current environments and AI visible

The DA0-002 concepts domain explicitly includes cloud providers, cloud and on-premises infrastructure, storage types, containerization, modern analysis environments, libraries, database management tools, and artificial intelligence concepts.

Its AI objective names generative AI, large language models, foundational models, deep learning, natural language processing, and robotic process automation. Learners should understand what these concepts are and how they relate to data work, without assuming that every tool-specific feature is tested.

Communication and troubleshooting receive clearer emphasis

DA0-002 places communication requirements inside Data Analysis. It asks candidates to consider user personas, technical versus non-technical audiences, accessibility, level of detail, sensitivity, and key performance indicators.

Troubleshooting also appears directly in Data Analysis and Visualization and Reporting. The objective language includes connectivity and basic SQL issues, source validation, logs, stale or corrupt data, filter problems, refresh performance, code and calculation review, peer review, and monitoring alerts.

Many foundational skills still transfer

The version change does not erase the core data lifecycle. Both objective maps require learners to understand data environments, acquire and prepare data, analyze results, communicate with visualizations, and apply governance and quality controls. Concepts such as data types, joins, cleansing, descriptive statistics, charts, access, and privacy remain relevant.

The practical distinction is coverage, not whether every older lesson is useless. Reuse a DA0-001 explanation when it accurately teaches a shared concept, but verify terminology, scope, and emphasis against DA0-002 before spending review time on it.

How to update an older study plan

Keep durable fundamentals from DA0-001 materials, such as data structures, acquisition, cleansing, statistics, visualization, and governance. Then map every resource against the DA0-002 objectives and fill the gaps with current material.

Do not rely on a cover title or marketplace description. Check the exam code printed inside the book, course, question bank, or PDF. If it says DA0-001, treat it as supplementary rather than complete DA0-002 preparation.

• Confirm that the resource says DA0-002.
• Add explicit study for AI concepts and current analysis tools.
• Practise audience-aware communication and accessibility decisions.
• Include troubleshooting for analysis, sources, calculations, filters, and refreshes.
• Use the DA0-002 weights when planning review time.