Does the model obtained by reverse engineering an existing database (from DDL and catalog introspection) directly yield a fully normalized, platform-agnostic logical data model?

Introduction / Context:
Reverse engineering extracts a structural view of an existing database. However, physical artifacts (surrogate keys, indexes, denormalizations, naming quirks) mean the result is typically a physical/conceptual hybrid, not a pure logical model aligned to business semantics.

Given Data / Assumptions:

• DDL reveals tables, columns, constraints, and sometimes comments.
• Physical design choices reflect performance or legacy constraints.
• Logical models abstract from platform specifics and emphasize semantics.

Concept / Approach:
The reverse-engineered output is a starting point. Analysts must remove purely physical concerns, consolidate synonyms, resolve overloaded columns, and reintroduce missing entities and relationships discovered via requirements analysis.

Step-by-Step Solution:
Extract schema objects and constraints.Map tables to entities and identify true business keys.Eliminate denormalizations; model many-to-many with association entities.Standardize names and data domains.Validate with stakeholders to align with business language.

Verification / Alternative check:
Walk through user stories with both models; mismatches highlight where the reverse-engineered schema diverges from business semantics.

Why Other Options Are Wrong:
Even BCNF sources may include platform-specific artifacts. ORM-generated schemas can still include compromises that are not logical-model constructs.

Common Pitfalls:
Accepting the physical schema as “the” logical truth; failing to capture business rules not enforced in DDL.

Final Answer:
Incorrect