hardMultiple Select
PDE Practice Question: A data pipeline reads thousands of JSON files…
A data pipeline reads thousands of JSON files from Cloud Storage, processes them with Cloud Dataflow, and writes to BigQuery. The pipeline sometimes fails because of malformed JSON records. Which three steps should the data engineering team take to improve pipeline reliability? (Choose THREE.)
⚠ Common exam trap
Many exam-takers confuse reactive monitoring (Option C) with proactive reliability improvements, or they assume a simple try-catch block (Option B) is sufficient in a distributed processing framework like Dataflow, where fault tolerance requires persistent retry mechanisms and dead-letter queues.
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
✓
Integrate Cloud Pub/Sub as an intermediary to buffer and allow message retry
Integrating Cloud Pub/Sub as an intermediary decouples the ingestion of JSON files from the Dataflow pipeline. Pub/Sub provides at-least-once delivery and automatic retries for messages that are not acknowledged, which buffers against transient failures and malformed records. This allows the pipeline to pull messages at its own pace and retry processing without losing 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.
- ✓
Integrate Cloud Pub/Sub as an intermediary to buffer and allow message retry
Why this is correct
Pub/Sub can retry delivery of messages, improving reliability.
- ✗
Use a try-catch block in the pipeline to retry processing failed records
Why it's wrong here
Retrying will not fix malformed JSON; must be handled differently.
- ✗
Create a Cloud Monitoring alert on pipeline failures
Why it's wrong here
Alerts only notify; do not improve pipeline reliability directly.
- ✓
Add schema validation before processing to reject invalid JSON records
Why this is correct
Early validation prevents malformed data from entering processing.
- ✓
Implement a dead-letter queue in the Dataflow pipeline to store failed records for later analysis
Why this is correct
Catches malformed records without failing the entire pipeline.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.