The correct choice is to use an orchestrator like Cloud Composer with DAG-level retry logic because the requirement demands that if any transformation step fails, the entire daily run must be retried from the beginning, ensuring atomicity of the batch ETL process. Cloud Composer, built on Apache Airflow, allows you to configure a retry entire run from beginning orchestrator pattern by setting the `dagrun_timeout` and `retries` parameters at the DAG level, so a failure in any task triggers a full restart rather than a partial retry. On the Google Professional Data Engineer exam, this scenario tests your understanding of workflow orchestration versus simple task-level retries—a common trap is choosing a solution like Cloud Dataflow with side-inputs or a pub/sub retry queue, which only re-run the failed step, not the entire batch. The key insight is that when data consistency demands a clean slate for each daily run, the orchestrator must treat the whole DAG as an atomic unit. Memory tip: think "DAG-level retry" versus "task-level retry"—if the question says "entire run from beginning," always pick the orchestrator that can restart the whole DAG.
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. 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 financial company needs to process batch trades data daily and ensure that if a transformation step fails, the entire daily run is retried from the beginning. Which design pattern is appropriate?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use an orchestrator like Cloud Composer with retry logic
Option B is correct because the requirement states that if any transformation step fails, the entire daily run must be retried from the beginning. An orchestrator like Cloud Composer (Apache Airflow) provides native DAG-level retry logic that can be configured to restart the entire workflow on failure, ensuring atomicity of the batch run. This pattern is essential for maintaining data consistency when partial processing cannot be tolerated.
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.
✗
Use idempotent writes with checkpointing
Why it's wrong here
Idempotent writes with checkpointing are useful for streaming or partial retries, but do not guarantee a full retry from the beginning.
✓
Use an orchestrator like Cloud Composer with retry logic
Why this is correct
Cloud Composer (Airflow) allows defining DAGs with retry policies on the entire pipeline, ensuring full restart on failure.
Related concept
Read the scenario before looking for a memorised answer.
✗
Retry the failed step only
Why it's wrong here
Retrying only the failed step may cause inconsistent state if other steps are not idempotent.
✗
Use a transactional staging area
Why it's wrong here
A staging area helps with atomicity but does not automate retry logic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that checkpointing or idempotent writes are sufficient for full-run retries, but the trap is that checkpointing enables partial resumption, not the complete restart from scratch that the question explicitly demands.
Detailed technical explanation
How to think about this question
In Apache Airflow (Cloud Composer), a DAG can be configured with `dagrun_timeout` and `retries` at the DAG level, so if any task fails, the entire DAG run is marked as failed and can be retried from the beginning via the scheduler or manual trigger. This differs from task-level retries, which only re-run the failed task. The key subtlety is that Airflow's `catchup` and `depends_on_past` settings can affect whether a full restart is truly atomic, so careful DAG design is required to ensure no partial state persists.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use an orchestrator like Cloud Composer with retry logic — Option B is correct because the requirement states that if any transformation step fails, the entire daily run must be retried from the beginning. An orchestrator like Cloud Composer (Apache Airflow) provides native DAG-level retry logic that can be configured to restart the entire workflow on failure, ensuring atomicity of the batch run. This pattern is essential for maintaining data consistency when partial processing cannot be tolerated.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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