Databricks-Spark-Assoc Pandas API on Spark Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  It applies my_func in a distributed manner across partitions without shuffling, and the function must return a scalar or a pandas Series.

  Why it's wrong here

  groupby().apply() in pandas API on Spark does not avoid shuffling when grouping by a column. It requires a shuffle to bring all rows of the same group into the same partition. The function can return a pandas DataFrame, Series, or scalar, but the execution involves a shuffle, which can be expensive for many groups. The statement incorrectly claims no shuffling and misstates return types.

• ✗

  It uses the Spark Catalyst optimizer to translate my_func into native Spark expressions, so no Python UDF is involved and performance is optimal.

  Why it's wrong here

  Pandas API on Spark does not translate arbitrary Python functions into Spark expressions. groupby().apply() uses a Python UDF under the hood, which incurs serialization and execution overhead. The Catalyst optimizer cannot introspect arbitrary Python code. While some built-in operations are optimized, custom functions in apply are opaque to Catalyst, so performance is generally slower than native Spark operations.

• ✓

  It triggers a full shuffle to group data, then applies my_func to each group as a pandas DataFrame on the executor, and the function's return type determines the result schema.

  Why this is correct

  groupby().apply() performs a shuffle to co-locate rows of the same group. On each executor, it converts each group into a pandas DataFrame and applies my_func. The return type—whether scalar, Series, or DataFrame—is inferred to build the output schema. This can be powerful but may cause memory issues if a single group is large, because the entire group must fit in memory as a pandas object.

• ✗

  It applies my_func to each partition independently without grouping, and the results are concatenated, which is efficient for large datasets.

  Why it's wrong here

  groupby().apply() is group-aware, not partition-aware. It must ensure that all rows of a given group are processed together, which requires a shuffle. Applying to each partition independently would produce incorrect results if a group spans multiple partitions. The operation is not efficient for large datasets when there are many groups, due to shuffle and per-group pandas conversion overhead.