PDE Ingesting and Processing the Data Practice Question
You are designing a Dataflow pipeline to process streaming data. The pipeline may encounter malformed records. You need to handle these errors without failing the entire pipeline and store the bad records for later analysis. What is the best practice?
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 a dead letter sink to write malformed records to a separate Pub/Sub topic or GCS location.
Dead letter sinks are a common pattern: route erroneous records to a separate output (e.g., Pub/Sub topic or GCS) for later investigation. Writing to BigQuery using Storage Write API with error handling is good, but for malformed records you want to isolate them. Raising exceptions would fail the pipeline. Logging only loses the data.
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 a dead letter sink to write malformed records to a separate Pub/Sub topic or GCS location.
Why this is correct
This is the recommended pattern: isolate bad records for later reprocessing while allowing the pipeline to continue.
- ✗
Catch the exception and log it, then continue processing.
Why it's wrong here
Logging loses the malformed record; you need to store it for analysis.
- ✗
Write all records to BigQuery using the Storage Write API and handle errors in the write operation.
Why it's wrong here
This doesn't handle malformed records before BigQuery; they may cause failures.
- ✗
Raise an exception in the DoFn to stop the pipeline for manual intervention.
Why it's wrong here
Stopping the pipeline is not desirable; it should be resilient.
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