Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A Spark job is running on Databricks and experiences a stage where tasks are taking much longer than expected. The Spark UI shows that some tasks have significantly higher shuffle read sizes than others, and the stage is skewed. Which Spark feature can automatically mitigate this skew by splitting large partitions into smaller ones?
⚠ Common exam trap
Test-takers frequently confuse skew mitigation with other performance features like Dynamic Resource Allocation or Speculative Execution, which do not split partitions to balance load.
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
✓
Adaptive Query Execution (AQE) with skew join optimization
Adaptive Query Execution (AQE) in Spark 3.x can dynamically detect and handle skew during shuffle operations. When enabled, it can split large skewed partitions into smaller ones, distributing the load more evenly across tasks. This directly addresses the skew observed in the stage, reducing task duration and improving overall job performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Columnar Shuffle with Kryo Serialization
Why it's wrong here
Columnar Shuffle and Kryo Serialization are optimization techniques for reducing shuffle data size and improving serialization performance. They do not handle data skew by splitting partitions. They can improve overall shuffle efficiency but are not designed to mitigate skew dynamically.
- ✗
Dynamic Resource Allocation
Why it's wrong here
Dynamic Resource Allocation adjusts the number of executors based on workload but does not address data skew within partitions. It can scale resources up or down, but if a few tasks are much larger due to skew, adding more executors may not help because the skewed tasks still take longer. It does not split partitions.
- ✓
Adaptive Query Execution (AQE) with skew join optimization
Why this is correct
AQE dynamically reoptimizes query plans based on runtime statistics. When it detects skewed partitions during a shuffle, it can split large partitions into smaller sub-partitions, balancing the load across tasks. This reduces the impact of skew and improves stage performance, making it the correct feature for this scenario.
- ✗
Speculative Execution
Why it's wrong here
Speculative Execution launches duplicate tasks for slow-running tasks to mitigate stragglers. While it can help with skew by running a copy of a slow task, it does not split large partitions; it simply duplicates the work. This can be resource-intensive and does not address the root cause of skew.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
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