PDE Ingesting and Processing the Data Practice Question
A data engineer needs to build a Dataflow pipeline that reads JSON messages from Pub/Sub, transforms them (including filtering, grouping, and enrichment), and writes the results to BigQuery. The pipeline must handle schema evolution in the input messages and minimize data loss. Which THREE settings or features should the engineer use? (Choose THREE.)
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 side inputs to enrich the data with reference data from BigQuery
Side inputs allow the pipeline to enrich streaming data with reference data from BigQuery, which is a common requirement for handling schema evolution and enrichment. Option C is correct: a dead letter queue captures messages that fail to parse or validate, preventing data loss and enabling reprocessing of failed messages. Option D is correct: autoscaling adjusts the number of workers dynamically to handle spikes in message volume, ensuring no data is lost due to backpressure. Option B is incorrect: setting `withAllowedLateness` to 0 does not help with schema evolution or minimize data loss; it simply drops late data, which could cause data loss. Option E is incorrect: Streaming Engine improves checkpoint performance but is not directly related to schema evolution or data loss minimization.
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 side inputs to enrich the data with reference data from BigQuery
Why this is correct
Correct. Side inputs enable enrichment of streaming data with reference data from BigQuery, supporting schema evolution by allowing dynamic lookup.
- ✗
Set the `withAllowedLateness` to 0 for windowing to minimize latency
Why it's wrong here
Incorrect. Setting `withAllowedLateness` to 0 drops late data, which can lead to data loss and does not address schema evolution.
- ✓
Set up a dead letter queue (DLQ) for messages that fail to parse or validate
Why this is correct
Correct. A dead letter queue (DLQ) captures messages that fail parsing or validation, preventing data loss and allowing reprocessing.
- ✓
Enable autoscaling to handle spikes in message volume
Why this is correct
Correct. Autoscaling dynamically adjusts worker count to handle spikes in message volume, minimizing data loss due to backpressure.
- ✗
Enable Streaming Engine to reduce checkpoint size
Why it's wrong here
Incorrect. Streaming Engine reduces checkpoint size but does not directly relate to schema evolution or data loss minimization.
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