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PDE Practice Question: A financial company needs to process batch trades…
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?
⚠ Common exam trap
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.
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
✓
Use an orchestrator like Cloud Composer with retry logic
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.
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.
- ✗
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.
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