- A
Add a DataflowJobStatusSensor in the DAG that waits for job completion and retries if failed.
Why wrong: A sensor only monitors status; it does not automatically retry. You would need additional logic to resubmit the job.
- B
Set the 'retries' parameter in the DAG's default_args to a positive integer.
This allows Airflow to retry the entire task (which launches the Dataflow job) if it fails due to transient errors.
- C
Configure the Dataflow pipeline to automatically retry on failure using the --numberOfWorkerHarnessThreads option.
Why wrong: This option controls threading, not retries. Dataflow does not automatically retry failed jobs; it retries failed workers within a job.
- D
Use a Cloud Function triggered by Cloud Scheduler to re-launch the pipeline if the Dataflow job fails.
Why wrong: This adds extra complexity and is not as straightforward as using Airflow's built-in retry mechanism.
PDE Maintaining and Automating Data Workloads Practice Question
This PDE practice question tests your understanding of maintaining and automating data workloads. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company runs a critical batch pipeline using Cloud Dataflow. The pipeline processes financial transactions and runs every hour. Recently, some runs have failed due to transient errors (e.g., network timeouts). The engineer wants to automatically retry failed runs without manual intervention. The pipeline is launched from a Cloud Composer DAG using DataflowPythonOperator. What is the BEST way to handle retries?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 in the DAG's default_args to a positive integer.
Option B is correct because Cloud Composer (Apache Airflow) natively supports task-level retries via the 'retries' parameter in default_args. When a DataflowPythonOperator fails due to a transient error, Airflow automatically re-executes the task up to the specified number of retries, without requiring custom sensors or external triggers. This is the simplest and most reliable mechanism for handling transient failures in a DAG-driven pipeline.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 DataflowJobStatusSensor in the DAG that waits for job completion and retries if failed.
Why it's wrong here
A sensor only monitors status; it does not automatically retry. You would need additional logic to resubmit the job.
- ✓
Set the 'retries' parameter in the DAG's default_args to a positive integer.
Why this is correct
This allows Airflow to retry the entire task (which launches the Dataflow job) if it fails due to transient errors.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure the Dataflow pipeline to automatically retry on failure using the --numberOfWorkerHarnessThreads option.
Why it's wrong here
This option controls threading, not retries. Dataflow does not automatically retry failed jobs; it retries failed workers within a job.
- ✗
Use a Cloud Function triggered by Cloud Scheduler to re-launch the pipeline if the Dataflow job fails.
Why it's wrong here
This adds extra complexity and is not as straightforward as using Airflow's built-in retry mechanism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Dataflow-level retry options (like --maxRetryAttempts) with Airflow task-level retries, or assume that a sensor or external trigger is required to detect and retry failures, when in fact Airflow's native retry parameter is the simplest and most appropriate solution for transient errors in a DAG-managed pipeline.
Detailed technical explanation
How to think about this question
Under the hood, Airflow's retry mechanism works by setting the 'retries' parameter in the DAG's default_args, which applies to all operators unless overridden. When a DataflowPythonOperator fails, Airflow marks the task instance as 'up_for_retry' and schedules it for re-execution after a configurable delay (retry_delay). This avoids the need for external monitoring services and ensures retries respect Airflow's backoff and concurrency settings, which is critical for batch pipelines that must complete within a specific time window.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Maintaining and Automating Data Workloads — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Maintaining and Automating Data Workloads — This question tests Maintaining and Automating Data Workloads — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the 'retries' parameter in the DAG's default_args to a positive integer. — Option B is correct because Cloud Composer (Apache Airflow) natively supports task-level retries via the 'retries' parameter in default_args. When a DataflowPythonOperator fails due to a transient error, Airflow automatically re-executes the task up to the specified number of retries, without requiring custom sensors or external triggers. This is the simplest and most reliable mechanism for handling transient failures in a DAG-driven pipeline.
What should I do if I get this PDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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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