PDE Maintaining and Automating Data Workloads Practice Question
A data engineer schedules a Cloud Composer 2 environment to run a DAG that triggers a Dataflow batch job every night. The DAG sometimes fails because the Dataflow job takes longer than the default task timeout. The engineer wants the DAG to wait for the Dataflow job to finish rather than timing out. Which change should the engineer make?
⚠ Common exam trap
Test-takers frequently confuse task retries or schedule changes with making the operator wait for an asynchronous job to finish.
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 Dataflow operator's execution_timeout and deferrable parameters so the task waits for job completion.
Cloud Composer 2 Dataflow operators can block until the job reaches a terminal state, and the deferrable variant frees the worker while waiting. Setting execution_timeout to a value longer than the expected job duration prevents premature task failure. Schedule interval, Cloud Function wrapping, and retry policies do not make a task wait for the underlying Dataflow job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a retry policy to the DAG so failed tasks are retried until the Dataflow job finishes.
Why it's wrong here
Retries re-run the task, which would resubmit the Dataflow job and potentially create duplicate jobs. Retries do not convert a timeout into a wait; they compound the problem. The correct approach is to configure the operator to wait for job completion rather than to retry the launch.
- ✗
Increase the DAG's schedule interval so runs start less frequently.
Why it's wrong here
Changing the schedule interval alters how often the DAG runs, not how long an individual task waits. The task would still hit its timeout while the Dataflow job is running. Schedule frequency is unrelated to task-level waiting behavior, so this does not solve the premature timeout.
- ✓
Set the Dataflow operator's execution_timeout and deferrable parameters so the task waits for job completion.
Why this is correct
The Dataflow operators in Cloud Composer can wait for the job to complete, and setting an appropriate execution_timeout prevents the task from failing early. Using the deferrable mode releases the worker slot while waiting, which is the recommended pattern for long-running jobs in Composer 2. This directly addresses the timeout problem.
- ✗
Move the Dataflow launch into a Cloud Function and call it from the DAG.
Why it's wrong here
Wrapping the Dataflow launch in a Cloud Function does not make the DAG wait for job completion; the function would return as soon as the job is submitted. The DAG task would still complete or time out independently of the Dataflow job's actual runtime. This adds complexity without solving the waiting requirement.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PDE question from scratch — 747 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.