GCP-ADP Data Pipeline Orchestration Practice Question
A task in your Airflow DAG involves a long-running process that might exceed the default Airflow worker pod timeout. How should you handle this?
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
✓
Set the 'execution_timeout' parameter on the task instance.
Adjust the 'execution_timeout' parameter for the specific task in the Airflow DAG definition to allow longer processing times.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Move the task to a custom node pool in GKE.
Why it's wrong here
Infrastructure location does not solve task-level Airflow timeouts.
- ✓
Set the 'execution_timeout' parameter on the task instance.
Why this is correct
This allows individual tasks to exceed default limits safely.
- ✗
Increase the 'default_dag_run_timeout'.
Why it's wrong here
This impacts the whole DAG, which may not be desired.
- ✗
Decrease the number of parallel tasks.
Why it's wrong here
This reduces load but does not change the timeout per task.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Google Cloud exam blueprint
This GCP-ADP practice question is part of Courseiva's free Google Cloud 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 GCP-ADP exam.