Databricks-DA-Assoc Executing Queries with Databricks SQL Practice Question
An analyst needs to combine data from two tables that share a common key. Which join type returns all rows from both tables, even if there is no match in the other table?
⚠ Common exam trap
Candidates frequently confuse FULL OUTER JOIN with INNER JOIN or LEFT JOIN, forgetting that only a full outer join preserves all records from both tables regardless of matches.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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FULL OUTER JOIN
A full outer join is the only join type that preserves every record from both source tables, filling in NULL values where matches do not exist. This is essential for comprehensive data auditing and reconciliation tasks where the analyst needs to identify missing records or orphans in either dataset. Understanding the impact of NULLs in the result set is crucial for accurate analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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INNER JOIN
Why it's wrong here
An INNER JOIN only returns rows where there is a match in both tables based on the join key. It excludes records that do not have a counterpart, which is the opposite of the requirement. This is used for finding strictly intersecting data, not for seeing all available information.
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LEFT JOIN
Why it's wrong here
A LEFT JOIN returns all rows from the left table and the matched rows from the right table. Rows in the right table that do not have a match are excluded. This join type fails the requirement to include unmatched rows from both the left and right sides simultaneously.
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FULL OUTER JOIN
Why this is correct
A FULL OUTER JOIN returns all rows from both tables, combining them where keys match and filling missing values with NULL for rows that have no match. This provides a complete view of all data in both tables, which is the intended behavior for comprehensive reconciliation or merger reports.
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CROSS JOIN
Why it's wrong here
A CROSS JOIN returns the Cartesian product of the two tables, creating a combination of every row in the first table with every row in the second. It does not use join keys and is not intended for matching records, making it inappropriate for merging data based on common keys.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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