Databricks-Spark-Assoc Spark Architecture and Components Practice Question
Exhibit
org.apache.spark.SparkException: Job aborted due to stage failure: Task ... in stage 1.0 (TID 1) had a shuffle fetch failure
Refer to the exhibit. Which of the following is the most likely cause for this 'shuffle fetch failure' in a Databricks cluster?
⚠ Common exam trap
Candidates often blame network congestion or code errors for fetch failures. They fail to consider that autoscaling or preemptible instances in Databricks are the most frequent causes of lost shuffle files.
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
✓
The map-side executor was terminated before the reduce-side task could fetch its data.
A shuffle fetch failure usually occurs when an executor loses the intermediate data that a downstream task is trying to read, often due to the executor being preempted or failing. In Databricks, this is a common symptom when autoscaling removes a node that held required shuffle files. Understanding this error is essential for debugging job stability and configuring cluster settings to ensure reliable data processing despite dynamic resource availability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Driver ran out of memory during task scheduling.
Why it's wrong here
If the Driver ran out of memory, the job would typically fail with an OOM error or a connection timeout, not a shuffle fetch failure. Shuffle fetch failures specifically point to missing map-side output files, which occur on worker nodes, not within the central Driver's memory space.
- ✓
The map-side executor was terminated before the reduce-side task could fetch its data.
Why this is correct
When an executor terminates before its shuffle files are fetched, the reduce task receives a fetch failure. This commonly happens in dynamic environments where nodes are reclaimed. Implementing a persistent shuffle service or adjusting task retry policies can mitigate this issue by ensuring data availability for downstream tasks.
- ✗
The transformation involves a narrow dependency.
Why it's wrong here
Narrow dependencies do not involve shuffle fetch operations, as they do not require data exchange between executors. Therefore, a fetch failure cannot occur in a purely narrow transformation pipeline. Only shuffles induce the network interactions that result in fetch errors if the source data is unavailable.
- ✗
The input data format is unsupported.
Why it's wrong here
Unsupported data formats typically trigger read errors at the beginning of a job, not mid-shuffle. Shuffle fetch failures occur after successful map stages, implying the input was read correctly. Attributing this to the file format ignores the distributed nature of the error, leading to incorrect debugging paths.
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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-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.