Databricks-Spark-Assoc Spark Architecture and Components Practice Question
A data engineer submits a PySpark job that performs a wide transformation via a join operation across two large datasets. During execution, several tasks in the shuffle stage fail repeatedly due to transient network timeouts between worker nodes. How does Apache Spark's architecture handle these failed tasks?
⚠ Common exam trap
Candidates often assume that an entire stage or job immediately fails upon any task failure, forgetting that Spark's driver implements a built-in retry mechanism specifically for individual tasks before declaring a stage failure.
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 driver node marks the specific task as failed and resubmits it for execution, respecting the maximum task retry configuration.
Spark's driver node manages task scheduling and monitors execution. When a task fails due to a transient error, the task scheduler automatically resubmits the exact same task up to a configured maximum number of retries before failing the entire stage or job. This ensures fault tolerance without requiring manual intervention for temporary infrastructure glitches during distributed shuffles.
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 cluster manager automatically terminates the entire Spark application and generates a fatal core dump on the driver node.
Why it's wrong here
Cluster managers handle resource allocation and container health, but they do not automatically kill the entire Spark application for a simple task failure. The Spark driver intercepts task failures and attempts local recovery before escalating.
- ✓
The driver node marks the specific task as failed and resubmits it for execution, respecting the maximum task retry configuration.
Why this is correct
The driver tracks task status across the DAG scheduler and task scheduler. When a task throws an exception or experiences a timeout, the scheduler flags it as failed and schedules a retry on available executor slots.
- ✗
All executors connected to the cluster are immediately restarted to clear corrupted memory partitions from the shuffle service.
Why it's wrong here
Restarting every executor discards cached partitions and in-flight shuffle data cluster-wide, which is disproportionate when only individual tasks fail. It is tempting because executor restarts clear corrupted state, but Spark's task scheduler instead retries the failed tasks on other executors, escalating only after repeated failures within the configured maximum attempts.
- ✗
The transformation is automatically converted from a wide transformation into a narrow transformation to avoid shuffle network traffic.
Why it's wrong here
Spark cannot rewrite a join's shuffle dependency into a narrow transformation; the wide versus narrow classification is fixed by whether data must be redistributed across partitions. It is tempting because narrow transformations avoid network traffic, which is the failing resource here, but Spark instead recovers by re-executing failed tasks from the shuffle lineage.
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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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