hardMultiple Choice
PDE Practice Question: A data pipeline ingests sensor data from IoT…
A data pipeline ingests sensor data from IoT devices via Cloud Pub/Sub, processes it with Cloud Dataflow, and writes to BigQuery. The pipeline is failing with high latency and data loss. Which troubleshooting step should be taken first?
⚠ Common exam trap
Google Cloud often tests the principle of 'diagnose before you optimize' — the trap here is that candidates jump to scaling or switching technologies (options C and D) without first checking logs, which is the fundamental first step in any troubleshooting workflow.
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
✓
Check Stackdriver logging for error messages.
Stackdriver (now Cloud Logging) is the first place to investigate when a Dataflow pipeline experiences high latency and data loss. Dataflow automatically logs errors, worker failures, and system messages to Cloud Logging, which can reveal root causes such as insufficient resources, stuck steps, or Pub/Sub subscription issues. Checking logs first avoids premature scaling or configuration changes that may not address the actual problem.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Check Stackdriver logging for error messages.
Why this is correct
Checking Stackdriver (Cloud Logging) first surfaces the pipeline's own error messages, which pinpoint whether latency and loss stem from Pub/Sub backlog, Dataflow worker exhaustion, or BigQuery write rejections. This satisfies the stem's "first" constraint by gathering diagnostic evidence before altering any component.
- ✗
Disable exactly-once processing in Dataflow.
Why it's wrong here
Disabling exactly-once processing removes deduplication guarantees, so it cannot fix latency or loss and would worsen duplicate or dropped records. Exactly-once is the right setting when downstream sinks require idempotent writes and the pipeline is otherwise stable.
- ✗
Increase the number of Dataflow workers.
Why it's wrong here
Scaling workers cannot recover messages already lost; the stem's data loss plus latency points to Pub/Sub backlog or Dataflow errors, so diagnose the pipeline's error logs and subscription metrics first. Adding workers suits sustained throughput shortfalls once the pipeline is healthy, not an active failure.
- ✗
Switch to BigQuery streaming inserts.
Why it's wrong here
Streaming inserts change the BigQuery write path, not the Dataflow or Pub/Sub cause of loss; they also cost more per row. Streaming inserts suit low-latency dashboards where Dataflow is already delivering records correctly and batch loads are too slow.
Go deeper
Related to this question
About these practice questions
This PDE question is part of Courseiva's 747-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.