DABCC

It is a multiple-choice exam covering the seven published domains. Expect single-answer questions, many framed as short workplace scenarios rather than definitions. Confirm the current delivery method (test centre or remote proctored), item count, and time allowance with the certifying body before you book, since these are set by them and change over time.

The breadth is the challenge, not trick questions. Candidates with three or more years across integration, modelling, and reporting usually find it manageable; specialists in one area often struggle with governance, metadata, and MDM. Scenario items reward people who have made real design trade-offs. Aim for comfortable coverage of all seven domains rather than mastery of two.

Most working professionals need a structured eight to twelve weeks of part-time study, weighted toward weaker domains. Build a domain checklist, read one standard text per area, and spend at least a third of your time on practice questions with written rationales. If you already work daily in ETL and modelling, shift hours toward governance, data quality dimensions, and MDM entity resolution.

Start with data architecture and modeling, because grain, keys, and normalisation underpin integration, quality, and analytics questions. Then take integration and ETL, followed by data quality management. Move to governance and stewardship and to security and privacy, which are policy-heavy and reward reading plus case discussion. Finish with metadata and MDM, which draws on all the earlier domains.

Confusing data quality dimensions (validity versus accuracy, completeness versus coverage), muddling fact and dimension grain, and treating governance as documentation rather than decision rights. Others misread slowly changing dimension types, conflate master data with reference data, and pick the most technically sophisticated security control instead of the risk-proportionate one. Read each stem carefully for the constraint being tested.

Retake rules, waiting periods, and any additional fees are set by the certifying body and are not fixed here. Check the official policy page before booking so you know how soon a second attempt is allowed and what it costs. Use the score report's domain breakdown to target the retake rather than repeating your existing study plan.

Neither the passing score nor the validity period is published here, because both are administrative details controlled by the certifying body and can change. Consult the official certification site or your candidate handbook for the current scaled score requirement, recertification cycle, and any continuing education requirement needed to maintain the credential.

No specific product is tested, but practical experience matters. Writing SQL, building a small star schema, defining data quality rules against a real table, and tracing lineage through one pipeline will make scenario questions concrete. If your role is non-technical, work through guided exercises so terms like surrogate key, late-arriving dimension, and idempotent load are familiar.

It signals breadth, which helps stewards and analysts who coordinate across teams and need credibility with architects and engineers. It is less useful if you only need a narrow vendor certification. Pair it with evidence of delivered work - a governance charter, a DQ scorecard, a published data dictionary - since the credential alone rarely changes a hiring decision.

Answer before reading options, then write down why each wrong option fails. Group misses by domain to find patterns, such as consistently choosing accuracy when validity is being tested. Revisit source material for any domain where you miss more than one in four, and re-test only after studying, not immediately.