Databricks-Spark-Assoc Pandas API on Spark Practice Question
A developer is using the Pandas API on Spark and wants to write efficient code. They are reviewing operations that can cause a full shuffle of data across the cluster. Which TWO operations should they be cautious about because they typically require a shuffle? (Choose two.)
⚠ Common exam trap
The trap here is assuming that any pandas-like operation on a distributed DataFrame is equally expensive, when in fact narrow transformations avoid shuffles.
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
✓
`psdf.groupby('region').sum()`
Operations that require global data reorganization, such as sorting and grouping, trigger shuffles. `sort_values` needs a global order, and `groupby` with aggregation needs rows with the same key co-located. By contrast, element-wise arithmetic, column selection, and `head` are narrow transformations that operate within partitions without moving data across the network.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
`psdf[['id', 'amount']]`
Why it's wrong here
Selecting a subset of columns is a projection that operates on each row independently. It is a narrow transformation and does not require a shuffle. Spark simply reads the requested columns from each partition. This is an efficient operation and not a source of cluster-wide data movement, so it should not be flagged as shuffle-heavy.
- ✓
`psdf.groupby('region').sum()`
Why this is correct
`groupby` followed by an aggregation requires data with the same key to be brought together, which necessitates a shuffle. Spark performs a shuffle to partition rows by the grouping key before aggregating. This is a fundamental distributed operation and is expensive when the number of distinct keys is large or when data is skewed. Caching or pre-partitioning can mitigate the cost.
- ✗
`psdf['amount'] + 10`
Why it's wrong here
Adding a constant to a column is a narrow transformation that operates on each row independently. It does not require data to move across the network. Spark can apply this expression within each partition. There is no shuffle involved, so it is not an operation that requires caution for shuffle cost, although it still triggers a job when an action is called.
- ✗
`psdf.head(5)`
Why it's wrong here
`head(5)` retrieves the first few rows and typically only needs to read one or a few partitions. It does not require a full shuffle. Spark can limit the scan and return the rows quickly. While it triggers a job, the data movement is minimal. It is not a shuffle-heavy operation, so it is not one of the operations to be cautious about in this context.
- ✓
`psdf.sort_values('amount')`
Why this is correct
`sort_values` requires a global ordering of rows, which means data must be repartitioned and sorted across the cluster. This triggers a shuffle. While the result is a distributed DataFrame, the shuffle is expensive for large datasets. Developers should consider whether a full sort is necessary or whether a distributed sort can be avoided by using approximate or partitioned approaches.
About these practice questions
This Databricks-Spark-Assoc question is part of Courseiva's 295-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.