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Rapid Recall — R36–R70

Prompts

  1. Name four modeling deliverables.
  2. Forward engineering sequence?
  3. Reverse engineering sequence?
  4. Name five conceptual-model build steps.
  5. Name six logical-model build steps.
  6. Name eight physical-model build/adaptation steps.
  7. Name six modeling tools/resources.
  8. Recite PRISM.
  9. Name three model-governance control areas.
  10. What five change-history questions should version control record?
  11. Name the ten Data Model Scorecard categories.
  12. Model vs diagram?
  13. Unary hierarchy vs unary network?
  14. Dependent vs independent entity?
  15. Give five domain forms and what each constrains.
  16. Star vs snowflake?
  17. Conformed dimension vs conformed fact?
  18. Reconstruct Data Vault hub/link/satellite and Anchor-modeling components.
  19. What extra behavior/access ideas distinguish OO/UML class modeling?
  20. What is the defining representation style of Fact-Based Modeling?
  21. View vs materialized view?
  22. Vertical vs horizontal partitioning?
  23. What problems do BCNF, 4NF, 5NF address at recognition level?
  24. What makes an associative entity necessary/useful?
  25. Subtype absorption vs supertype partition?
  26. What business rule must be preserved when a surrogate replaces a natural/composite PK?
  27. Name four core modeling deliverables beyond “the picture.”
  28. What is a data model pattern?
  29. What is an industry data model, and what must happen before local use?
  30. Logical naming vs physical naming?
  31. Distinguish modeling, lineage, profiling, Metadata-repository, pattern, industry-model resources.
  32. Model review vs model maintenance?
  33. What does the Data Model Scorecard test beyond diagram readability?
  34. Why is source-target mapping/lineage a modeling deliverable?
  35. Name major physical-design adaptation decisions and explain why they are requirement-driven.

Reconstruction Key R36–R70

  1. Diagram/model, definitions, issues/outstanding questions, lineage.
  2. Requirements → CDM → LDM → PDM.
  3. Existing DB → PDM → LDM → CDM.
  4. Scheme; notation; initial user-view CDM; reconcile enterprise terminology/rules; sign-off.
  5. Requirements; existing artifacts; associative entities; atomic attributes; domains; keys/detailed relationships.
  6. Resolve abstractions; add physical attribute details; reference data; surrogate keys when needed; denormalize if justified; index; partition; views/materialized structures.
  7. Modeling tools; lineage tools; profiling tools; Metadata repositories; model patterns; industry models.
  8. Performance/ease of use; Reusability; Integrity; Security; Maintainability.
  9. Standards; design reviews; version/change control.
  10. Why; What/How; When; Who; Where.
  11. Requirements; completeness; scheme/level fit; structural soundness; generic structures; naming; readability; definitions; enterprise consistency; Metadata matches data.
  12. Model = full representation; diagram = one visual view.
  13. Hierarchy = same-type instance has at most one parent; network = may have multiple parents.
  14. Dependent identity relies on parent-contributed key attribute(s); independent can be uniquely identified without that identifying dependency.
  15. Type = basic kind; format = pattern; list = enumerated set; range = boundaries; rule-based = conditional validity.
  16. Star = collapsed dimensions; snowflake = normalized dimension component/hierarchy tables.
  17. Conformed dimension = shared descriptive context; conformed fact = shared standardized measure.
  18. Data Vault: hubs, links, satellites. Anchor: anchors, attributes, ties, knots.
  19. Attributes plus operations/methods, encapsulation and access visibility such as public/internal/private.
  20. Objects, facts and roles, often verbalized in controlled natural language; attributes not primary construct.
  21. Standard view derives on request; materialized view stores an instantiated result refreshed at a predetermined time.
  22. Vertical splits columns; horizontal splits rows by values/ranges.
  23. BCNF strengthens functional-dependency rules; 4NF addresses multivalued dependencies; 5NF addresses join dependencies.
  24. It resolves M:M or higher-arity relationship and can carry facts about the relationship itself.
  25. Absorption = one supertype table with subtype-specific nullable columns; partition = separate subtype tables with inherited attributes.
  26. Preserve original natural/business uniqueness as alternate key/constraint if still a business requirement.
  27. Model/diagram; definitions; issues/questions; lineage/source mapping.
  28. Reusable generic modeling structure for recurring situations.
  29. Broad pre-built industry reference; customize/validate against local requirements before use.
  30. Logical favors business-readable full terms; physical adapts to DBMS conventions/constraints with traceability.
  31. Modeling tools create/manage models; lineage traces source/target; profiling reveals actual data; Metadata repository preserves shared knowledge; patterns/industry models provide reusable starting content.
  32. Review = point-in-time correctness/completeness/consistency; maintenance = ongoing synchronization as requirements/implementation change.
  33. Requirements capture, completeness, scheme fit, structural soundness, generic structures, naming, readability, definitions, enterprise consistency, Metadata-data agreement.
  34. It identifies where modeled data originates/how it maps to implementation and supports validation + impact analysis.
  35. Resolve abstractions; physical details/reference data; key implementation; denormalization; indexes; partitions; views — selected from actual requirements/performance/security/access constraints, not habit.

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