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Data Modeling Quiz (100 Questions)

Data Modeling Quiz

Fundamental Concepts (1–10)

1. What is a data model?





2. In data modeling, what is an entity?





3. Which of the following best describes an attribute in an ER model?





4. What is the purpose of a relationship in an ER diagram?





5. What is a primary key?





6. What is a foreign key used for?





7. Which of the following is a common benefit of using a conceptual data model?





8. What does cardinality describe in a relationship?





9. What characterizes data in First Normal Form (1NF)?





10. What is the main goal of applying Second Normal Form (2NF)?





Basic to Moderate Relational Concepts (11–20)

11. Moving from 2NF to Third Normal Form (3NF) aims to:





12. In a logical data model, entities usually translate to what in the physical database?





13. A conceptual data model generally does not include:





14. Why might you denormalize a data model?





15. In dimensional modeling, a “fact” table typically contains:





16. A “dimension” table in a star schema provides:





17. What is a “snowflake” schema?





18. Slowly Changing Dimensions (SCD) are used to:





19. A foreign key constraint helps maintain:





20. Normalization’s main purpose is to:





Intermediate Normalization & Advanced Relational Design (21–30)

21. Boyce-Codd Normal Form (BCNF) primarily focuses on:





22. Fourth Normal Form (4NF) deals with:





23. Fifth Normal Form (5NF) aims to:





24. A surrogate key is often used when:





25. A composite key is:





26. Check constraints are used to:





27. An index in a relational database is primarily used to:





28. A unique constraint ensures:





29. Referential integrity means:





30. Cascading deletes occur when:





Conceptual, Logical, Physical Models & Modeling Tools (31–40)

31. A conceptual data model focuses on:





32. A logical data model includes:





33. A physical data model describes:





34. UML class diagrams can be used for data modeling by:





35. A data dictionary is:





36. ERD (Entity-Relationship Diagram) notations commonly include:





37. Crow’s Feet notation in ER diagrams represents:





38. A database schema is:





39. ER modeling is often preferred over UML class diagrams when:





40. Data modeling tools (like ERwin, ER/Studio) help by:





Non-Relational & Specialized Models (41–50)

41. Hierarchical data models store data as:





42. The network data model differs from the hierarchical model in that:





43. Key-value stores in NoSQL systems store data as:





44. Document stores (e.g., MongoDB) represent data in:





45. Column-family databases (e.g., Cassandra) organize data by:





46. Graph databases (e.g., Neo4j) model data as:





47. In graph modeling, “properties” are:





48. A triple store (RDF store) models data as:





49. Polyglot persistence refers to:





50. Sharding in non-relational systems involves:





Advanced Warehousing & BI Modeling (51–60)

51. In data warehousing, ETL stands for:





52. The main purpose of a star schema is to:





53. A conformed dimension is one that:





54. Late-arriving dimensions are handled by:





55. Slowly Changing Dimension Type 2 involves:





56. A junk dimension consolidates:





57. Degenerate dimensions are attributes stored in:





58. Data Vault modeling aims to:





59. Surrogate keys in dimensional modeling are preferred because:





60. Bus architecture in data warehousing refers to:





Data Governance, MDM, and Metadata Management (61–70)

61. Master Data Management (MDM) focuses on:





62. Data lineage describes:





63. Metadata in data modeling is:





64. A data catalog helps users by:





65. Data stewardship is responsible for:





66. Data quality rules ensure:





67. Reference data management involves:





68. Business Glossaries are important because:





69. Data governance programs typically aim to:





70. A common challenge in MDM is:





Advanced Topics, Techniques & Emerging Trends (71–80)

71. Temporal data modeling deals with:





72. Event modeling focuses on:





73. Domain-driven design (DDD) in data modeling emphasizes:





74. Data modeling for machine learning often involves:





75. Ontologies and taxonomies in semantic data modeling:





76. Data mesh architecture suggests:





77. Streaming data modeling focuses on:





78. Multi-model databases support:





79. Data virtualization is about:





80. Metadata-driven modeling leverages:





Cutting-Edge & Specialized Practices (81–90)

81. In graph modeling, “labels” are used to:





82. RDF (Resource Description Framework) is used to:





83. Modeling data for data lakes often requires:





84. Data wrangling (data preparation) techniques help by:





85. Agile data modeling promotes:





86. Data modeling for cloud-native systems often involves:





87. Geospatial data modeling involves:





88. Hybrid transaction/analytical processing (HTAP) systems require models that:





89. JSON schema validation in document databases is used to:





90. Microservices architectures influence data modeling by:





Ultra-Advanced & Emerging Trends (91–100)

91. Knowledge graphs combine:





92. Data modeling for AI-driven data governance tools helps by:





93. Event sourcing in data modeling involves:





94. Data modeling for IoT (Internet of Things) requires:





95. Using graph embeddings in data modeling refers to:





96. Data Fabric architectures rely on modeling to:





97. Vertical partitioning in physical modeling:





98. Data anonymization techniques in data modeling:





99. Applying graph constraints (like uniqueness of node labels) ensures:





100. Synthetic data generation in data modeling is used when: