Databricks-Spark-Assoc Pandas API on Spark Practice Question
A data engineer has a Pandas-on-Spark DataFrame `psdf` with a default index. They call `psdf.sort_values('amount', ascending=False)`. After the operation, they notice that the resulting DataFrame's index values no longer match the original row positions. Which statement best describes the behavior of the index after sorting?
⚠ Common exam trap
The trap here is assuming that sorting resets the index, as some might expect from SQL ORDER BY or from Spark DataFrame operations that produce a new row order without a persistent index.
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
✓
The index values are reordered along with the rows, so the original index labels remain attached to their corresponding rows but are no longer sorted.
Sorting in Pandas API on Spark reorders rows but preserves the original index labels, which become unsorted. This is consistent with pandas semantics and ensures that index-based alignment still works. The index is not reset, dropped, or replaced with partition identifiers unless the user explicitly performs such an operation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The index is dropped entirely, and the resulting DataFrame has no index.
Why it's wrong here
Pandas API on Spark always maintains an index unless explicitly dropped. Sorting does not remove the index; it only reorders the rows and their associated index labels. Dropping the index would require a separate operation such as reset_index(drop=True).
- ✓
The index values are reordered along with the rows, so the original index labels remain attached to their corresponding rows but are no longer sorted.
Why this is correct
In Pandas API on Spark, sort_values sorts the rows while carrying the index labels with them. The index is not reset; it simply becomes unsorted. This matches pandas behavior and is important when merging or aligning data, as the index labels still identify the original rows.
- ✗
The index is reset to a sequential integer range starting at 0, preserving the new row order.
Why it's wrong here
Pandas API on Spark does not automatically reset the index after sort_values. The index retains the original values, which are now out of order relative to the new row positions. A sequential integer range would only appear if the user explicitly calls reset_index, which is not implied by the scenario.
- ✗
The index is converted to a distributed sequence based on Spark partition IDs, ensuring efficient subsequent operations.
Why it's wrong here
The index in Pandas API on Spark is not automatically converted to partition-based identifiers after sorting. While the underlying Spark DataFrame may have partitions, the logical index remains the original labels. Partition IDs are internal and not exposed as the index unless using specific functions like attach_distributed_sequence.
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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.