Databricks-Spark-Assoc Pandas API on Spark Practice Question
You are developing a data pipeline using Pandas API on Spark. You need to perform operations that are efficient and avoid unnecessary data shuffling. Which two of the following operations are considered expensive because they may trigger a full shuffle or collect data to the driver? (Choose two.)
⚠ Common exam trap
The trap here is assuming that all pandas-like operations are equally efficient, but operations that require data shuffling are significantly more expensive.
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('column').agg({'value': 'sum'})`
Sorting and grouping aggregations are expensive in Pandas API on Spark because they require shuffling data across the cluster. Sorting needs a global order, and grouping needs to co-locate rows with the same key. In contrast, element-wise operations and filters are narrow and do not shuffle data, making them efficient.
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.head(5)`
Why it's wrong here
This is incorrect because `head(5)` is not expensive. It only collects a small number of rows (5) to the driver, which is a cheap operation. It does not require a full shuffle or scan of the entire dataset. It is designed for quick inspection and is safe to use even on large DataFrames.
- ✗
`psdf.filter(psdf['value'] > 100)`
Why it's wrong here
This is incorrect because `filter` is a narrow transformation that does not require shuffling. It simply applies a predicate to each row and can be executed within each partition independently. It is an inexpensive operation that does not move data across the network.
- ✗
`psdf['new_column'] = psdf['value'] * 2`
Why it's wrong here
This is incorrect because assigning a new column based on an existing column is a narrow transformation. It does not require shuffling data; each partition can compute the new column independently. This operation is cheap and efficient, as it only involves local computations per row.
- ✓
`psdf.groupby('column').agg({'value': 'sum'})`
Why this is correct
This is correct. `groupby` followed by an aggregation typically requires shuffling data so that all rows with the same key are on the same partition. This shuffle can be expensive, especially if the cardinality of the grouping column is high. However, it is a common operation and can be optimized with proper partitioning.
- ✓
`psdf.sort_values(by='column')`
Why this is correct
This is correct. `sort_values` in Pandas API on Spark triggers a global sort, which requires shuffling all data across partitions to order the rows correctly. This is an expensive operation because it involves a full data exchange. It should be used with caution on large datasets, as it can lead to significant network and disk I/O.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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