Databricks-DE-Pro Cost and Performance Optimization Practice Question
A data engineer is tuning a Spark job that reads from a Delta table and writes to another Delta table. The job uses a groupByKey operation followed by an aggregation. The engineer notices that the job is spilling to disk during the shuffle and taking a long time. The engineer wants to reduce shuffle spill and improve performance. Which action is most likely to help?
⚠ Common exam trap
The trap here is assuming that more partitions or more memory will solve shuffle spill, when the real fix is reducing the amount of data shuffled by using map-side aggregation.
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
✓
Replace groupByKey with reduceByKey or aggregateByKey to perform map-side aggregation.
Replacing groupByKey with reduceByKey or aggregateByKey enables map-side aggregation, which reduces the volume of data shuffled and thus reduces spill and improves performance. Other options either add overhead, do not address the root cause, or are costly workarounds that do not fix the fundamental inefficiency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of shuffle partitions to reduce the amount of data per partition.
Why it's wrong here
Increasing shuffle partitions can reduce the data size per partition, but it also increases the number of tasks and can lead to more overhead and smaller files. If the spill is due to skew or large records, simply adding partitions may not help and could worsen performance by creating many tiny tasks. It does not address the root cause of spilling, which is often insufficient memory per partition or data skew.
- ✗
Increase the executor memory and enable off-heap memory for the shuffle.
Why it's wrong here
Adding executor memory can temporarily alleviate spill, but it is a costly workaround that does not fix the underlying issue of shuffling too much data. Off-heap memory may help in some cases, but it is not a targeted solution for the inefficiency of groupByKey. The job will still shuffle large amounts of data, and the problem may recur as data volume grows.
- ✓
Replace groupByKey with reduceByKey or aggregateByKey to perform map-side aggregation.
Why this is correct
groupByKey shuffles all key-value pairs without map-side aggregation, causing large data transfers and spill. reduceByKey or aggregateByKey perform partial aggregation on the map side before shuffling, significantly reducing the amount of data shuffled and the memory pressure. This directly reduces spill and improves performance for aggregation workloads, and it is a best practice in Spark.
- ✗
Set spark.sql.shuffle.partitions to a very high value and enable adaptive query execution.
Why it's wrong here
A very high number of shuffle partitions can reduce per-partition data size but often leads to excessive small tasks and scheduling overhead. Adaptive query execution can coalesce partitions, but if the number is set too high, it may not fully mitigate the overhead. This approach does not address the fundamental inefficiency of groupByKey, which shuffles all data without map-side aggregation.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Pro exam.