- A
Configure the pipeline to drop malformed messages silently
Why wrong: Dropping silently makes debugging difficult.
- B
Use a side input to filter out malformed messages
Why wrong: Side inputs are for enriching data, not for error handling.
- C
Use a dead letter sink to write malformed messages to Cloud Storage or Pub/Sub for later analysis
This allows reprocessing without blocking the main pipeline.
- D
Raise an exception in the DoFn to fail the pipeline immediately
Why wrong: Failing the pipeline stops all processing, which is not desired.
- E
Log the error and continue processing the next message
Logging allows monitoring and debugging while maintaining pipeline continuity.
Question 526 of 1,000
PDE Ingesting and Processing the Data Practice Question
This PDE practice question tests your understanding of ingesting and processing the data. 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 company is building a data pipeline that ingests streaming data from Pub/Sub, transforms it with Dataflow, and loads it into BigQuery. They want to handle malformed messages that cannot be parsed. Which TWO actions should they implement for error handling? (Choose 2)
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 messages to Cloud Storage or Pub/Sub for later analysis
Option C is correct because a dead letter sink (e.g., writing malformed messages to Cloud Storage or a separate Pub/Sub topic) allows the pipeline to continue processing valid data while preserving the problematic records for offline inspection, retries, or debugging. This pattern is a standard best practice in streaming pipelines to avoid data loss and enable recovery without blocking the main data flow.
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.
- ✗
Configure the pipeline to drop malformed messages silently
Why it's wrong here
Dropping silently makes debugging difficult.
- ✗
Use a side input to filter out malformed messages
Why it's wrong here
Side inputs are for enriching data, not for error handling.
- ✓
Use a dead letter sink to write malformed messages to Cloud Storage or Pub/Sub for later analysis
Why this is correct
This allows reprocessing without blocking the main pipeline.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Raise an exception in the DoFn to fail the pipeline immediately
Why it's wrong here
Failing the pipeline stops all processing, which is not desired.
- ✓
Log the error and continue processing the next message
Why this is correct
Logging allows monitoring and debugging while maintaining pipeline continuity.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that raising an exception (Option D) is acceptable for error handling in streaming pipelines, but the correct approach is to isolate failures using a dead letter sink (Option C) while logging errors (Option E) to maintain pipeline continuity.
Detailed technical explanation
How to think about this question
In Apache Beam (the programming model behind Dataflow), the dead letter pattern is implemented by writing failed records to a separate PCollection (e.g., via a tagged output or a side output) and then sinking that collection to a durable storage system like Cloud Storage or a Pub/Sub topic. This approach leverages Beam's fault-tolerance and exactly-once processing guarantees, ensuring that malformed messages are not lost even if the pipeline restarts. In practice, you might also include metadata such as the original timestamp and error reason to aid in reprocessing.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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Ingesting and Processing the Data — study guide chapter
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FAQ
Questions learners often ask
What does this PDE question test?
Ingesting and Processing the Data — This question tests Ingesting and Processing the Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a dead letter sink to write malformed messages to Cloud Storage or Pub/Sub for later analysis — Option C is correct because a dead letter sink (e.g., writing malformed messages to Cloud Storage or a separate Pub/Sub topic) allows the pipeline to continue processing valid data while preserving the problematic records for offline inspection, retries, or debugging. This pattern is a standard best practice in streaming pipelines to avoid data loss and enable recovery without blocking the main data flow.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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