FDST-BDS study plan

Map every data-governance concept in Domain 1 to a one-page outline covering stewardship roles, policy lifecycle, decision rights, and operating models.

Distinguish accountability from responsibility by writing three worked examples of RACI assignments for a data domain such as customer or product.

Define the terms data owner, data steward, data custodian, and data trustee, and explain in writing how each differs in authority and typical day-to-day duties.

Draft a one-page data-governance charter skeleton with scope, principles, escalation path, and success measures that you can reuse in later weeks.

Build a policy-to-control traceability table linking three sample policies to the controls and metrics that would prove compliance.

Identify the governance touchpoints in the data-quality lifecycle, including who approves quality rules and who owns remediation.

Write a short case analysis of a stewardship escalation, showing how a steward, owner, and council would each act.

Compare centralized, federated, and hybrid governance operating models and justify a choice for a mid-size organization.

Classify conceptual, logical, and physical models and produce one example of each for the same business scenario.

Apply normalization through third normal form to a sample schema and then justify where denormalization is appropriate.

Draw an entity-relationship diagram with at least eight entities, including cardinality and optionality notation.

Explain dimensional modeling basics: fact vs dimension tables, grain, star vs snowflake, and slowly changing dimensions.

Contrast relational, document, key-value, and columnar storage with a stated use case for each.

Define the six core data-quality dimensions and write a testable rule for each against a sample dataset.

Design a data-quality scorecard with thresholds, owners, and escalation triggers.

Describe root-cause analysis for a recurring defect and propose a prevention control rather than a correction.

Distinguish data cleansing, standardization, enrichment, and deduplication with one example each.

Compare batch, streaming, and change-data-capture integration patterns and state when each is preferred.

Explain ETL vs ELT and the trade-offs in transformation timing, cost, and governance.

Identify the main data-interoperability standards and formats and describe how schema evolution is handled.

Map the core security controls: authentication, authorization, encryption at rest and in transit, masking, and tokenization.

Explain key privacy concepts including purpose limitation, minimization, consent, retention, and subject-access handling.

Describe the layers of a warehouse architecture from staging through presentation and the purpose of each.

Explain the difference between a data warehouse, a data mart, and a data lake, and when a lakehouse pattern applies.

Define core BI concepts: semantic layer, metrics definitions, drill-through, and governed self-service.

Outline how slowly changing dimensions and surrogate keys affect historical reporting accuracy.

Distinguish technical, business, and operational metadata and give two examples of each.

Explain the purpose of a data catalog and how lineage supports impact analysis.

Describe master data management: domains, matching and survivorship rules, and the difference between registry and consolidation styles.

Explain reference data and how it differs from master data, with a concrete example.

Distinguish descriptive, diagnostic, predictive, and prescriptive analytics and match each to a business question.

Explain how analytics outputs depend on governed, high-quality inputs and name the failure modes when they are not.

Complete two full timed practice sets across all eight domains and log every miss by domain and cause.

Re-review your weakest two domains using the logs and close each gap with a written correction note.