Databricks-Spark-Assoc Pandas API on Spark Practice Question
A developer is working with a Pandas API on Spark DataFrame `psdf` that has a default index. They call `psdf.sort_values('amount')` and then attempt to use `.loc` with an integer label to retrieve a specific row. They find that the integer label does not correspond to the row position they expect. What is the most likely explanation?
⚠ Common exam trap
The trap here is assuming that the default integer index behaves like a positional index and stays aligned with row order after sorting.
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 default index in the Pandas API on Spark is a sequence index that is not guaranteed to match row order after operations like `sort_values`.
The default index in the Pandas API on Spark is a synthetic sequence that is not renumbered after operations like `sort_values`. As a result, integer labels do not reliably map to row positions. To access rows by position, use `.iloc`. To make labels meaningful, explicitly set or reset the index before relying on label-based access.
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 default index in the Pandas API on Spark is a sequence index that is not guaranteed to match row order after operations like `sort_values`.
Why this is correct
The default index is a synthetic sequence that is assigned per partition and then combined. After a shuffle-inducing operation like `sort_values`, the index values are not renumbered to reflect the new row order. Therefore, using an integer label with `.loc` does not reliably retrieve the row at that position. This is a key difference from local pandas, where `sort_values` preserves the original index labels.
- ✗
The `.loc` indexer in the Pandas API on Spark always uses positional indexing, so integer labels are treated as row positions.
Why it's wrong here
`.loc` is label-based, not positional. The positional indexer is `.iloc`. The confusion arises because the default index looks like integers, but `.loc` still treats them as labels. If the labels are not aligned with the sorted order, `.loc` will not return the row at a given position. The correct tool for position-based access is `.iloc`.
- ✗
The default index is a distributed index that is only valid on the driver, so `.loc` cannot use it on executors.
Why it's wrong here
The default index is a distributed sequence index that exists across partitions. It is not driver-only. The issue is not validity on executors but the fact that the index values are not synchronized with row order after a shuffle. `.loc` works with the distributed index, but the labels may not correspond to the expected sorted positions.
- ✗
`sort_values` in the Pandas API on Spark resets the index by default, so integer labels always match the new sorted positions.
Why it's wrong here
`sort_values` does not reset the index by default. It sorts the data and leaves the index labels attached to their original rows, which may end up out of order. If a reset were performed, the labels would be renumbered sequentially, but that is not the default behavior. This misconception leads to incorrect assumptions about label alignment after sorting.
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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.