Databricks-Spark-Assoc Using Spark SQL Practice Question
Which of the following describes the behavior of a 'Left Outer Join' in Spark SQL?
⚠ Common exam trap
Candidates often confuse Left Outer Joins with Full Outer Joins, mistakenly thinking the result set includes non-matching rows from BOTH tables rather than just the left table.
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
✓
It returns all rows from the left table and matched rows from the right table.
A Left Outer Join is fundamental in SQL for merging datasets where you want to keep all records from the left table, regardless of whether a match exists in the right table. Understanding this ensures that data integration processes correctly handle non-matching records, preventing data loss during joins. It is a critical concept for analysts and developers building reports that require comprehensive views of primary entities with optional supplemental info.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It returns only rows that have matches in both the left and right tables.
Why it's wrong here
This describes an Inner Join. An Inner Join requires a match on both sides; a Left Outer Join intentionally returns all rows from the left side, filling in missing right-side values with NULLs, which is a fundamentally different outcome that serves a different purpose in data analysis.
- ✓
It returns all rows from the left table and matched rows from the right table.
Why this is correct
The definition of a Left Outer Join is that it preserves the entire left side of the join. For rows that do not have a corresponding key in the right table, Spark fills the right-side columns with NULLs, allowing the developer to see the full list from the left dataset.
- ✗
It returns all rows from the right table and matched rows from the left table.
Why it's wrong here
This describes a Right Outer Join. While similar in logic to a Left Outer Join, it shifts the focus to the right table. Using the wrong outer join type can result in unexpected data outcomes, making it vital to correctly identify the directionality required for the specific business logic.
- ✗
It excludes all rows from both tables that do not have a matching key.
Why it's wrong here
Excluding all non-matching rows is a characteristic of an Inner Join. An outer join, by definition, is designed to include non-matching rows to provide a complete view of the data, which is essential for audit trails or reporting on missing entries that might otherwise be hidden by strict joins.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.