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PDE Practice Question: A data engineer is designing a batch ETL pipeline…
A data engineer is designing a batch ETL pipeline using Cloud Composer and Dataflow. The pipeline must be self-healing and retry on failures. Which Composer feature should they configure?
⚠ Common exam trap
Google Cloud often tests the distinction between orchestration-level retries (Composer DAG) and execution-level retries (Dataflow), leading candidates to pick Dataflow retries (Option D) when the question explicitly asks for a Composer feature.
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
✓
Retry policy on the DAG
Cloud Composer (based on Apache Airflow) allows you to configure a retry policy directly on the DAG or individual tasks. This enables the pipeline to automatically retry failed tasks according to parameters like `retries`, `retry_delay`, and `retry_exponential_backoff`, making the ETL pipeline self-healing without external services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Cloud Tasks for retries
Why it's wrong here
Cloud Tasks queues asynchronous HTTP callbacks and is unrelated to Composer DAG orchestration, so it cannot retry a failed Dataflow task. Airflow's own retries parameter on the operator is the mechanism; Cloud Tasks would suit decoupled service-to-service dispatch instead.
- ✓
Retry policy on the DAG
Why this is correct
A retry policy on the DAG defines retries, delay and backoff for failed tasks, so transient Dataflow or API errors are re-attempted automatically. This delivers the self-healing behaviour the pipeline requires without manual intervention or external orchestration.
- ✗
Cloud Composer with high availability
Why it's wrong here
High availability replicates Composer's Airflow components across zones to survive zone outages; it does not retry failed tasks. The stem requires task-level retry behaviour, which is set via Airflow retries, retry_delay and related operator parameters, not through the environment's HA configuration.
- ✗
Dataflow retries
Why it's wrong here
Dataflow retries are configured on the Dataflow job itself, not as a Composer orchestration feature, so they do not make the Composer DAG self-healing when a task fails. Task-level retries belong in the Airflow operator; Dataflow retries suit transient worker failures within a running job.
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This PDE 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 PDE exam.