Databricks-Spark-Assoc Pandas API on Spark Practice Question
A data analyst is using the Pandas API on Spark to compute summary statistics. They call `psdf.describe()` on a large DataFrame and notice the job takes much longer than expected. They want to understand why this operation is more expensive than a similar operation on a small local pandas DataFrame. What is the primary reason?
⚠ Common exam trap
The trap here is assuming that pandas-like syntax implies local, in-memory execution rather than distributed Spark jobs.
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
✓
`describe()` triggers a full scan and computes multiple aggregations that may require shuffling data across partitions.
`describe()` on a Pandas API on Spark DataFrame runs a distributed Spark job that scans all rows and computes multiple aggregates. Percentiles require approximate quantile algorithms that shuffle data across partitions. This is fundamentally more expensive than local pandas, which operates on in-memory data on a single machine without network shuffles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
`describe()` is a lazy operation that only builds a plan, so the delay is from plan construction rather than execution.
Why it's wrong here
Plan construction in Spark is fast and does not cause noticeable delays. `describe()` returns a DataFrame, but the delay the analyst observes is from job execution, not lazy plan building. The expensive part is the distributed scan and aggregation, especially the percentile computation that requires a shuffle.
- ✓
`describe()` triggers a full scan and computes multiple aggregations that may require shuffling data across partitions.
Why this is correct
`describe()` computes count, mean, stddev, min, max, and percentiles for numeric columns. Percentiles in Spark are computed with approximate algorithms that require a shuffle to gather distribution information across partitions. The full scan plus the multi-aggregation plan and the shuffle for quantiles explain the increased runtime on a distributed DataFrame.
- ✗
`describe()` caches the DataFrame in memory by default, and the caching step is what takes the extra time.
Why it's wrong here
`describe()` does not automatically cache the input DataFrame. Caching is an explicit action the user must take. While caching can speed up repeated access, it is not the default behavior of `describe()`. The extra time is due to the aggregation plan and shuffle, not an implicit cache operation.
- ✗
`describe()` converts the entire DataFrame to a local pandas DataFrame on the driver before computing statistics.
Why it's wrong here
The Pandas API on Spark does not collect the whole dataset to the driver for `describe()`. It computes statistics in a distributed manner using Spark aggregations. Collecting to the driver would be catastrophic for large data and is not how this method is implemented. The slowdown comes from distributed computation, not from local collection.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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