PDE Maintaining and Automating Data Workloads Practice Question
Your team uses Cloud Composer to orchestrate a daily ETL workflow that extracts data from an on-premises database, transforms it in Dataproc, and loads it into BigQuery. The workflow occasionally fails due to transient network issues. You want to automatically retry failed tasks without manual intervention. What should you do?
⚠ Common exam trap
The trap here is thinking that Cloud Composer has an environment-wide retry setting, but retries must be configured per task.
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 retries parameter and retry_delay on the Airflow tasks in the DAG.
Airflow tasks in Cloud Composer support retries and retry_delay parameters, which allow automatic retries of failed tasks. This is the correct way to handle transient failures. Other options either do not address task-level retries or could cause data duplication.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the Dataproc cluster to automatically restart on failure.
Why it's wrong here
Dataproc clusters do not automatically restart on failure; they are ephemeral and managed by the workflow. Restarting the cluster would not retry the failed task. Moreover, the failure could occur in any task, not just Dataproc. This approach does not address the need for task-level retries and would not prevent workflow failures.
- ✗
Enable automatic retries on the Cloud Composer environment.
Why it's wrong here
Cloud Composer environments do not have a setting to automatically retry failed tasks at the environment level. Retries are configured at the task level within the DAG. Enabling environment-level retries is not a feature and would not provide the desired control. Therefore, this option is incorrect.
- ✗
Use Cloud Scheduler to trigger the DAG multiple times until it succeeds.
Why it's wrong here
Cloud Scheduler triggers the DAG on a schedule, but it does not retry individual tasks. Triggering the entire DAG multiple times could lead to duplicate data processing and does not handle transient failures within a run. It also lacks the granularity to retry only the failed tasks. This is not a recommended practice for handling task failures.
- ✓
Set the retries parameter and retry_delay on the Airflow tasks in the DAG.
Why this is correct
In Cloud Composer, Airflow tasks support retries and retry_delay parameters. By setting these, you can automatically retry failed tasks after a specified delay. This is the standard way to handle transient failures in Airflow DAGs. You can also set exponential backoff for retries. This approach requires no additional infrastructure and is fully integrated with Cloud Composer.
Go deeper
Related to this question
About these practice questions
This PDE question is part of Courseiva's 747-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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 Google Cloud exam blueprint
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.