Quick Answer
The answer is to configure a retry policy on the DAG. This is correct because Cloud Composer, built on Apache Airflow, allows you to define retry parameters directly at the DAG or task level, such as `retries`, `retry_delay`, and `retry_exponential_backoff`, which automatically re-execute failed tasks without requiring external monitoring services. On the Google Professional Data Engineer exam, this concept tests your understanding of how to build self-healing Dataflow pipelines within Composer, often appearing as a distractor against options like Cloud Tasks or Pub/Sub retry mechanisms—a common trap is confusing Composer’s built-in retry with external queuing systems. The key insight is that Composer’s retry policy is native to Airflow’s scheduler, making it the simplest way to achieve self-healing for batch ETL. Memory tip: think “DAG retries = built-in Band-Aid” for automatic failure recovery.
This PDE practice question tests your understanding of designing data processing systems. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Use Cloud Tasks for retries
Why wrong: Cloud Tasks is not integrated with Composer for ETL retries.
Retry policy on the DAG
Composer DAGs can have retry policies for tasks.
Cloud Composer with high availability
Why wrong: HA ensures infrastructure uptime, not task retries.
Dataflow retries
Why wrong: Dataflow has its own retry mechanisms but not part of Composer.
Answer choices
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 is not integrated with Composer for ETL retries.
Retry policy on the DAG
Why this is correct
Composer DAGs can have retry policies for tasks.
Related concept
Read the scenario before looking for a memorised answer.
Cloud Composer with high availability
Why it's wrong here
HA ensures infrastructure uptime, not task retries.
Dataflow retries
Why it's wrong here
Dataflow has its own retry mechanisms but not part of Composer.
Common exam traps
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.
Detailed technical explanation
Under the hood, Airflow's retry mechanism uses the `BaseOperator` parameters `retries` (integer) and `retry_delay` (timedelta), and optionally `retry_exponential_backoff` to implement exponential backoff. When a task fails, the scheduler checks the retry count and re-queues the task instance; if all retries are exhausted, the task is marked as failed. In a real-world scenario, transient errors like network timeouts or temporary resource exhaustion are handled gracefully without manual intervention, ensuring the batch ETL pipeline completes reliably.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Retry policy on the DAG — Option B is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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