File (table) joins: under what condition can two data files be joined into a third result file or view in data processing and relational querying?

Introduction / Context:
Joining datasets is central to relational processing. A “join” combines rows from two sources based on matching values in one or more columns. Understanding the prerequisite for a meaningful join helps avoid Cartesian products and incorrect results.

Given Data / Assumptions:

• We are creating a third file (or result set) from two inputs.
• Relational join semantics apply (equality or other predicates on columns).
• The usual case is an equi-join on a shared key.

Concept / Approach:
To join two files (tables) sensibly, they must share at least one common field (a column) on which to match. This “join key” aligns related records. Without a common field, the result is either a meaningless Cartesian product or requires an artificial predicate. Saying “they have a row in common” is imprecise, and requiring “no records with the same value” contradicts equi-join logic.

Step-by-Step Solution:
Identify necessary metadata: a shared attribute (field) with comparable values.Define the join condition: e.g., A.CustomerID = B.CustomerID.Execute the join to produce the third file/result set.

Verification / Alternative check:
SQL syntax (SELECT … FROM A JOIN B ON A.key = B.key) illustrates reliance on a common field. Natural joins also depend on identically named columns.

Why Other Options Are Wrong:

• They have a row in common: Rows cannot be “shared” across tables; matching is by fields.
• No records with the same value: That would prevent matches in equi-joins.
• Both (b) and (c): Self-contradictory because (c) negates joinability.
• None of the above: Having a common field is correct.

Common Pitfalls:
Confusing primary keys with foreign keys; ignoring data type mismatches (e.g., joining integer to text without casting) that break joins.

Final Answer:
they have a field in common