Databricks Certification Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Book

The correct approach to perform a parallelized groupBy operation across Spark worker nodes using Pandas API is via applyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.

As per the Databricks documentation:

"applyInPandas() allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution."

Option A is correct and achieves this parallel execution.

Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.

Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.

Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.

Therefore, to run a groupBy with parallel Pandas logic on Spark workers, Option A using applyInPandas is the only correct answer.

Apache Spark is a distributed data processing engine designed for large-scale, cluster-based computation.

Advantages:

Horizontal Scalability: Spark can distribute tasks across many machines, handling datasets larger than the memory of a single node.

Fault Tolerance: Spark automatically recovers from node or task failures using the lineage graph (RDD recovery mechanism) and retry logic.

These two features allow Spark to process huge datasets efficiently and reliably, unlike standard Python scripts that are limited to one machine and fail on single-node errors.

Why the other options are incorrect:

B: Spark runs on commodity hardware; no specialized machines required.

C: Spark emphasizes in-memory processing, not disk-only operations.

D: Spark still requires user code in Python, Scala, SQL, or Java.

Underutilization with timeout warnings often indicates insufficient parallelism — meaning there aren’t enough executors to process all tasks concurrently.

Solution:

Increase the number of executors to allow more parallel task execution and better resource utilization.

Example configuration:

--conf spark.executor.instances=8

This distributes the workload more effectively across cluster nodes and reduces idle time for pending tasks.

Why the other options are incorrect:

A: Extending timeouts hides the symptom, not the root cause (lack of executors).

B: More memory per executor won’t fix scheduling bottlenecks.

C: Reducing partition size may increase overhead and does not fix resource imbalance.

The number of tasks is controlled by the number of partitions. By default, spark.sql.shuffle.partitions is 200. If stages are showing very few tasks (less than total cores), you may not be leveraging full parallelism.

From the Spark tuning guide:

"To improve performance, especially for large clusters, increase spark.sql.shuffle.partitions to create more tasks and parallelism."

Thus:

A is correct: increasing shuffle partitions increases parallelism

B is wrong: it further reduces parallelism

C is invalid: increasing dataset size doesn’t guarantee more partitions

D is irrelevant to task count per stage

Final Answer: A