Design from existing tables: evaluate the assumption.\n\n"When building a database from existing tables, we may safely assume that there are no multivalued dependencies (MVDs) in the provided data."

Introduction / Context:
Many projects begin with data inherited from spreadsheets or legacy systems. The question probes whether it is safe to assume that such data contain no multivalued dependencies (MVDs), which can lead to redundancy and anomalies if left unaddressed. Recognizing and handling MVDs is critical for a robust design.

Given Data / Assumptions:

• You receive existing tables or files that must be turned into a well-designed database.
• MVDs occur when two or more independent multi-valued attributes coexist for the same key (for example, a person with multiple skills and multiple hobbies stored in one table).
• Normalization to 4NF is used to address MVDs by separating independent multi-valued facts.

Concept / Approach:
You should profile and analyze incoming data, discovering functional and multivalued dependencies rather than assuming their absence. Assumptions lead to designs that keep hidden redundancies. Detecting MVDs involves checking whether attributes vary independently with respect to a chosen key, which implies a need for decomposition into separate relations.

Step-by-Step Solution:

Verification / Alternative check:
Run data profiling queries to detect many-to-many patterns within a single table (for example, COUNT DISTINCT combinations vs. independent distinct counts). Unexpectedly high redundancy signals MVDs.

Why Other Options Are Wrong:

• Claims that MVDs are rare or vanish with a primary key ignore the nature of independent multi-valued attributes.
• Vendor dependence is irrelevant; MVDs are logical, not product-specific.

Common Pitfalls:
Treating CSV columns with delimited lists as normal attributes; believing that foreign keys alone cure redundancy without proper decomposition.

Final Answer:
Invalid (you must test for multivalued dependencies; never assume)