Quick Answer
The correct first step is to check Cloud Logging for error messages. This is because Dataflow automatically writes detailed logs about worker failures, system errors, and pipeline bottlenecks to Cloud Logging, making it the fastest way to troubleshoot dataflow high latency and data loss without guessing at root causes. On the Google Professional Data Engineer exam, this tests your understanding that observability must precede action—many candidates mistakenly jump to scaling workers or adjusting pipeline parameters, but logs often reveal simpler issues like Pub/Sub subscription backlogs or stuck transforms. A common trap is assuming high latency always means insufficient resources, when it could be a misconfigured window or a failed side input. Remember the mnemonic: Logs First, Scale Last—always diagnose before you optimize.
This PDE practice question tests your understanding of designing data processing systems. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Check Stackdriver logging for error messages.
Identifies root cause.
Disable exactly-once processing in Dataflow.
Why wrong: Can cause data loss.
Increase the number of Dataflow workers.
Why wrong: May mask underlying issue.
Switch to BigQuery streaming inserts.
Why wrong: Design change, not troubleshooting.
Answer choices
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.
Option A is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Identifies root cause.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Disable exactly-once processing in Dataflow.
Why it's wrong here
Can cause data loss.
Increase the number of Dataflow workers.
Why it's wrong here
May mask underlying issue.
Switch to BigQuery streaming inserts.
Why it's wrong here
Design change, not troubleshooting.
Common exam traps
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.
Detailed technical explanation
Under the hood, Dataflow uses a managed service that relies on Cloud Logging to surface detailed job metrics, including autoscaling decisions, worker health, and step-level throughput. A common subtle behavior is that Dataflow's streaming engine may drop late data if the watermark is not properly configured, and only log inspection reveals the exact timestamps and rejected elements. In real-world scenarios, a misconfigured Pub/Sub subscription with a short ack deadline can cause message redelivery and apparent data loss, which is immediately visible in Cloud Logging as repeated 'ack deadline exceeded' entries.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Designing data processing systems — This question tests Designing data processing systems — Read the scenario before looking for a memorised answer..
The correct answer is: Check Stackdriver logging for error messages. — Option A is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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