Reverse engineering outcome When you reverse engineer an existing relational database into a model, the resulting representation is typically a:

Introduction / Context:
Reverse engineering extracts structure from an existing database to create documentation and a foundation for redesign. The target audience needs an understandable, technology-agnostic view of entities and relationships—not raw storage internals.

Given Data / Assumptions:

• The source is a live schema with tables, keys, and constraints.
• We wish to capture entities, attributes, and relationships independent of file layouts or hardware.
• The model will inform normalization, naming standards, and application integration.

Concept / Approach:

A logical data model abstracts physical details while keeping implementable structures: tables (entities), columns (attributes), primary/foreign keys (relationships), and cardinalities. Conceptual models are higher level and often precede implementation; internal/physical models focus on storage, partitions, and indexes. Reverse engineering typically produces a logical model closest to what exists.

Step-by-Step Solution:

Verification / Alternative check:

Modeling tools (e.g., ER/Studio, ERwin) export logical ER diagrams from existing schemas, confirming the typical reverse-engineering outcome.

Why Other Options Are Wrong:

• A: Conceptual models omit many implementation details needed for redesign.
• B: Internal models are too low-level and vendor-specific for initial analysis.
• D/E: Do not reflect standard data modeling deliverables.

Common Pitfalls:

• Copying vendor datatypes verbatim instead of mapping to logical domains.
• Failing to capture unique keys and alternate keys critical for integrity.

Final Answer:

Logical model (tables, keys, and relationships abstracted from physical specifics).