Databricks-Spark-Assoc Using Spark SQL Practice Question
Which clause is used in a Spark SQL query to limit the number of rows returned by a query, and in which logical order is it executed?
⚠ Common exam trap
Candidates often assume LIMIT is applied before sorting, which would result in non-deterministic data. They fail to realize the logical execution order is crucial for consistent results.
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
✓
LIMIT, executed after ORDER BY.
The LIMIT clause is a common SQL operation used to restrict result sets. In Spark SQL, it is essential to understand that it is applied after sorting if an ORDER BY is present, or simply on the result stream if not. Using LIMIT is a critical performance practice to avoid overwhelming the driver when previewing large datasets in a notebook environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
LIMIT, executed after ORDER BY.
Why this is correct
In SQL, LIMIT is applied after the final result set has been determined, including any sorting required by the ORDER BY clause. This ensures that if you request the top 10 rows, you receive the 10 rows with the highest or lowest values as determined by the specified column ordering.
- ✗
TAKE, executed before ORDER BY.
Why it's wrong here
TAKE is a method in the Spark DataFrame API, not a valid SQL clause. The SQL standard uses LIMIT to restrict rows. Furthermore, attempting to limit before ordering would return an arbitrary subset of data, which is rarely the desired result for analytical queries requiring ordered top-n results.
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FETCH, executed before WHERE.
Why it's wrong here
While some SQL dialects use FETCH FIRST for limiting, the standard Spark SQL keyword is LIMIT. Moreover, any limiting clause must be applied after the WHERE clause has filtered the records; otherwise, you would limit the total dataset before filtering, leading to potentially empty or incorrect result sets.
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TOP, executed after GROUP BY.
Why it's wrong here
TOP is a T-SQL syntax keyword not supported in standard Spark SQL. Even if it were supported, the logical order of operations requires filtering and grouping to occur before any row-limiting operation. This ensures that the limit applies to the final aggregated result set, not the raw input data.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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