Databricks-Spark-Assoc Pandas API on Spark Practice Question
A data engineer is using Pandas API on Spark and needs to perform a join between two Pandas-on-Spark DataFrames `psdf1` and `psdf2` on a common column `id`. They write the following code:
```python result = psdf1.merge(psdf2, on='id', how='inner') ```
Which statement best describes the execution and potential issue with this operation?
⚠ Common exam trap
The trap here is assuming that merge in Pandas API on Spark behaves like pandas and operates in-memory on a single node, when it actually triggers a distributed Spark join.
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 merge is executed as a distributed sort-merge join, which may require a shuffle of both DataFrames across the network.
The merge operation in Pandas API on Spark translates to a Spark join, which typically involves shuffling data to co-locate matching keys. This distributed execution allows handling large datasets but can introduce network overhead. The actual join strategy depends on data size and Spark configuration, but a shuffle is common.
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 merge is performed locally on the driver after collecting both DataFrames, which can cause out-of-memory errors.
Why it's wrong here
Pandas API on Spark does not collect DataFrames to the driver for merge operations unless explicitly using `toPandas()`. The merge is executed in a distributed manner using Spark's join capabilities. Collecting to the driver would defeat the purpose of using Pandas API on Spark for large data.
- ✗
The merge automatically broadcasts the smaller DataFrame to all nodes, avoiding a shuffle.
Why it's wrong here
While Spark may use broadcast join optimization if one DataFrame is small enough (as determined by `spark.sql.autoBroadcastJoinThreshold`), this is not automatic for all merges. The default threshold is 10MB, and if both DataFrames are large, a sort-merge join with shuffle is used. The statement is not universally true and depends on data size and configuration.
- ✓
The merge is executed as a distributed sort-merge join, which may require a shuffle of both DataFrames across the network.
Why this is correct
Pandas API on Spark translates merge operations into Spark joins. For an inner join on a column, Spark typically uses a sort-merge join, which shuffles both DataFrames to co-locate matching keys. This shuffle can be expensive but is necessary for distributed execution. The operation is generally scalable but may be slow if data is skewed.
- ✗
The merge requires that both DataFrames have the same number of partitions, otherwise it fails with an error.
Why it's wrong here
Spark joins do not require the same number of partitions. Spark will repartition the DataFrames as needed for the join. There is no strict requirement for partition count equality. The operation may still be inefficient if partitions are unbalanced, but it will not fail solely due to partition count mismatch.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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