DABCC study plan

Define data governance operating models (centralized, federated, hybrid) and map each to when it fits an organization's size and regulatory load

Draft a RACI that assigns decision rights for data ownership, stewardship, and custodianship across business and IT roles

Build a glossary of 25 core governance terms (policy, standard, control, stewardship tier) with a one-sentence example for each

Distinguish conceptual, logical, and physical models and state the audience and artifact produced at each level

Normalize a sample dataset to third normal form and document each decomposition decision

Draw a dimensional model (fact plus conformed dimensions) for a retail or claims scenario and label grain explicitly

Compare star and snowflake schemas on query performance, storage, and maintenance trade-offs in written form

Classify slowly changing dimension types 1, 2, and 3 and pick the correct type for three stated business requirements

Contrast full-load, incremental, and change-data-capture patterns and select one for a stated latency and volume profile

Write pseudocode for an idempotent incremental load with watermarking and late-arriving-record handling

Design an ETL error-handling strategy covering reject tables, retry logic, and reconciliation counts

Explain orchestration dependencies and how to rerun a failed pipeline without duplicating rows

Define six data-quality dimensions and write a measurable rule for each against a concrete dataset

Distinguish validity, accuracy, completeness, and consistency with examples that show where they diverge

Design a data-quality scorecard with thresholds and an escalation path for breaches

Apply root-cause analysis to a simulated duplication incident and propose a preventive control

Classify data into sensitivity tiers and map each tier to handling, retention, and access requirements

Apply least-privilege and role-based access design to a scenario with three user personas

Compare masking, tokenization, pseudonymization, and encryption by use case and residual risk

Summarize core obligations under GDPR and CCPA/CPRA, including data-subject rights and breach-notification triggers

Outline the phases of a data-breach response and the evidence to preserve at each phase

Translate five business questions into report specifications with metrics, grain, filters, and refresh cadence

Choose between a pre-aggregated table and a live query for three scenarios and justify each choice

Identify three ways a visualization can mislead and state the correction for each

Write the calculation logic for a year-over-year and a rolling-average metric, including handling of partial periods

Distinguish technical, business, and operational metadata and list the artifact each produces

Define a master-data domain, its golden-record survivorship rules, and the matching attributes used to deduplicate

Compare registry, consolidation, and coexistence MDM implementation styles by cost and centralization

Draft a metadata repository entry for one dataset covering owner, lineage, sensitivity tier, and quality rules

Rehearse weak areas by re-answering missed practice items and writing the reason each wrong option fails

Connect domains by tracing one dataset from source through quality, security, and reporting in a written walkthrough

Complete a timed mixed-domain practice set under exam pacing and log error categories

Review the error log and close the top three recurring gaps with targeted re-study