Identifying demographics and relationships in a dataset is crucial for:

Question

Options

• AData cleaning
• BError handling
• CData modeling
• DDatabase normalization

• C
  96% (22)
• D
  4% (1)

Explanation

Identifying demographics and relationships in a dataset is the foundation of data modeling (C) - the process of defining how data entities are structured, how they relate to each other, and what attributes represent those entities. Understanding who the data is about (demographics) and how records connect (relationships) directly shapes the design of tables, entities, and associations in a data model.

• A (Data cleaning) is about fixing errors, duplicates, and inconsistencies in existing data - it doesn't require understanding relationships, just data quality rules.
• B (Error handling) refers to managing exceptions in code or processes, unrelated to understanding dataset structure.
• D (Database normalization) is a sub-technique used within data modeling to reduce redundancy, but identifying demographics and relationships is a broader, earlier-stage activity that precedes normalization.

Memory tip: Think "model = map." Before you can map a dataset into a schema, you must know who the data describes (demographics) and how they connect (relationships) - that's data modeling, not cleaning or normalizing.

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