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Rapid Recall — R1–R35

Answer aloud or on paper before expanding the key below.

Prompts

  1. Define data modeling.
  2. Name the three model levels.
  3. Name four business reasons data models matter.
  4. Name the four types of data modeled.
  5. Name the four core model components.
  6. Entity vs entity instance?
  7. Name the seven common entity categories.
  8. Name three qualities of a good entity definition.
  9. Cardinality vs arity?
  10. Unary vs binary vs ternary?
  11. What is a foreign key?
  12. What is an attribute?
  13. Simple vs composite vs compound key?
  14. Surrogate key?
  15. Super vs candidate key?
  16. Primary vs alternate key?
  17. Identifying vs non-identifying relationship?
  18. What is a domain? Name five domain forms.
  19. Name the six modeling schemes.
  20. What are relational design objectives?
  21. Fact table vs dimension table?
  22. Define grain.
  23. Recite SCD Type 1/2/3.
  24. Conformed dimension vs conformed fact?
  25. What is distinctive about UML class models?
  26. What is distinctive about Fact-Based Modeling?
  27. Data Vault: hub, link, satellite?
  28. Name four NoSQL types.
  29. CDM vs LDM vs PDM?
  30. Canonical model?
  31. View vs materialized view?
  32. Vertical vs horizontal partition?
  33. Normalization vs denormalization?
  34. Recite 1NF, 2NF, 3NF.
  35. Generalization vs specialization?

Reconstruction Key R1–R35

  1. Discover, analyze, scope, represent, and communicate data requirements precisely in a data model.
  2. Conceptual, logical, physical.
  3. Common vocabulary; explicit knowledge; project communication; application customization/integration/replacement; also scope/knowledge retention.
  4. Category, resource, business event, detail transaction.
  5. Entity, relationship, attribute, domain.
  6. Entity/type = Student; instance = one Student occurrence.
  7. Who, What, When, Where, Why, How, Measurement.
  8. Clarity, accuracy, completeness.
  9. Cardinality = zero/one/many instance participation; arity = number of participating entity types.
  10. One entity type / two / three.
  11. Relational representation of a relationship by carrying a related key into another entity/table.
  12. Property that identifies, describes, or measures an entity.
  13. Simple = one attribute; composite = 2+; compound = composite whose components are foreign keys.
  14. System-generated meaningless technical identifier.
  15. Super = any unique set; candidate = minimal unique set.
  16. One candidate selected primary; other candidates alternate.
  17. Parent PK becomes part of child PK vs parent key becomes non-PK FK.
  18. Complete valid value set; data type, format, list, range, rule-based.
  19. Relational, dimensional, object-oriented, fact-based, time-based, NoSQL.
  20. Exact expression of business data; reduce redundancy / keep one fact in one place.
  21. Measurements vs descriptive query/report context.
  22. Meaning/description of one fact-table row.
  23. Type 1 Overwrite; Type 2 New Row; Type 3 New Column.
  24. Shared descriptive context vs standardized measure definition.
  25. UML class includes attributes plus operations/methods and encapsulation/access.
  26. Objects/facts/roles, controlled natural-language verbalization, no attributes as primary modeling construct.
  27. Hub = business concept/key; link = relationship/integration; satellite = descriptive context/history.
  28. Document, key-value, column-oriented, graph.
  29. High-level concepts; detailed tech-independent requirements; technology-specific solution.
  30. Generic data-in-motion/message structure between systems.
  31. Virtual/on-demand view vs instantiated/materialized result.
  32. Columns vs rows.
  33. Remove redundancy/stabilize logic vs deliberately add redundancy for justified physical need.
  34. 1NF atomic/no repeats/valid key; 2NF full dependence on minimal complete key; 3NF no non-key dependencies.
  35. Common features up to supertype vs distinguishing features down to subtypes.

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