Databricks-Spark-Assoc Troubleshooting and Tuning DataFrame Apps Practice Question
When a Spark job is stuck in a shuffle phase, what is the most effective first step to identify the root cause of the performance bottleneck?
⚠ Common exam trap
Candidates immediately restart the cluster or rewrite business logic instead of inspecting task metrics in the Spark UI to isolate data skew.
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
✓
Check the Spark UI 'Stages' tab for uneven task durations.
The Spark UI provides a detailed breakdown of stages, tasks, and memory usage. Examining the 'Stages' and 'SQL' tabs reveals which tasks are taking the longest, which is indicative of skew or resource contention. Learning to interpret the Spark UI is the single most important skill for a Databricks developer, as it turns opaque 'stuck' jobs into actionable data regarding partition distribution and executor behavior.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Restart the cluster to clear any cached data.
Why it's wrong here
Restarting the cluster is a destructive action that wipes out temporary files and cache, masking the underlying issue rather than identifying it. It is an unnecessary step that does not provide any insight into why the task was stuck in the first place, leading to repeated failures later.
- ✓
Check the Spark UI 'Stages' tab for uneven task durations.
Why this is correct
The Spark UI allows you to see the duration and data size of every task in a stage. If one task takes significantly longer than others, it is a clear indicator of data skew. This allows the developer to isolate the specific partition causing the delay and implement a remediation strategy.
- ✗
Increase the spark.executor.memory value.
Why it's wrong here
Blindly increasing memory is a reactive, non-analytical approach. Without confirming that memory is the actual bottleneck via metrics, you are wasting cluster resources and potentially making the job slower by increasing garbage collection times within the JVM. Diagnostic analysis must always precede resource modification in a production environment.
- ✗
Enable speculative execution immediately.
Why it's wrong here
Speculative execution should only be enabled when there are known stragglers caused by hardware issues. Enabling it without diagnosing the bottleneck can lead to unnecessary resource consumption and could potentially exacerbate shuffle-related issues by launching duplicate tasks that compete for the same network and disk bandwidth on the cluster.
About these practice questions
Courseiva writes every Databricks-Spark-Assoc question from scratch — 295 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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-Spark-Assoc 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-Spark-Assoc exam.