Databricks-Spark-Assoc Using Spark SQL Practice Question
You need to combine two datasets in Spark SQL, retaining all records from the left table and matching records from the right table, while filling unmatched right columns with null values. Which join type should you use?
⚠ Common exam trap
Candidates frequently confuse LEFT OUTER JOIN with RIGHT OUTER JOIN or FULL OUTER JOIN, accidentally swapping the order of tables or including unwanted nulls from the left side.
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
✓
LEFT OUTER JOIN
A left outer join ensures every single record from the left dataset appears in the final result set, regardless of whether a matching key exists in the right dataset. Missing matches on the right side are populated with null values, maintaining structural integrity for downstream 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.
- ✗
RIGHT OUTER JOIN
Why it's wrong here
Retaining all records from the right table and matching records from the left table is the specific behavior of a right outer join. Using this join type reverses the priority of data retention away from the required left dataset.
- ✗
FULL OUTER JOIN
Why it's wrong here
Returning all records from both left and right tables while filling unmatched rows on either side with nulls is achieved via full outer joins. This includes records that have no matches in either direction, expanding the result set further.
- ✓
LEFT OUTER JOIN
Why this is correct
Preserving all rows from the primary left table and augmenting them with matching values from the right table defines the left outer join. Unmatched columns are automatically padded with null values, exactly meeting the business requirement.
- ✗
INNER JOIN
Why it's wrong here
Filtering out any rows that do not find a matching join key in both tables is the behavior of an inner join. Records lacking matches on either side are completely dropped from the resulting dataset, violating the retention rule.
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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.