- A
Set the updateCompatibility flag to true and restart the pipeline.
Why wrong: C is wrong because updateCompatibility is for checking compatibility of new pipeline version, not for stuck jobs.
- B
Increase the persistent disk size for all workers to reduce I/O contention.
Why wrong: A is wrong because disk size is rarely the cause of no progress in Dataflow.
- C
Examine the worker logs in Cloud Logging for any error messages or exceptions.
B is correct because logs can reveal the root cause, such as out-of-memory errors or stuck transforms.
- D
Force stop the pipeline and update it with a new version using the --update flag.
Why wrong: E is wrong because force stopping may cause data loss; it's better to diagnose without stopping.
- E
Enable Dataflow Streaming Engine to move state to the backend and reduce worker load.
D is correct because Streaming Engine can alleviate progress issues caused by state contention.
Quick Answer
The answer is to enable Dataflow Streaming Engine and examine worker logs in Cloud Logging. Enabling Streaming Engine moves pipeline state from worker VMs to the backend service, which alleviates worker memory pressure and prevents the "no progress" stall caused by local state exhaustion. This is correct because a stuck streaming job often results from workers running out of heap space or hitting garbage collection thrash when holding large state in-memory, and Streaming Engine offloads that state to a persistent backend, allowing workers to focus purely on computation. On the Google Professional Data Engineer exam, this scenario tests your understanding of Dataflow’s architecture trade-offs—specifically that Streaming Engine is the designed fix for stuck pipelines, not just a performance optimization. A common trap is to immediately restart the job or increase machine type, but the exam expects you to first check logs for root causes like serialization failures or OOM errors, then apply Streaming Engine as the structural remedy. Memory tip: "Stuck state? Stream it away—check logs, then Engine saves the day."
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing 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 Dataflow streaming job is processing data from Pub/Sub and writing to BigQuery. The job is stuck with the message 'No progress has been made' for several minutes. Which TWO actions should the team take to troubleshoot and resolve the issue? (Choose TWO.)
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
Examine the worker logs in Cloud Logging for any error messages or exceptions.
Option C is correct because examining worker logs in Cloud Logging is the first step to identify the root cause of a stuck pipeline. Common issues like out-of-memory errors, serialization failures, or worker crashes are logged there, and without inspecting logs, troubleshooting is guesswork.
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.
- ✗
Set the updateCompatibility flag to true and restart the pipeline.
Why it's wrong here
C is wrong because updateCompatibility is for checking compatibility of new pipeline version, not for stuck jobs.
- ✗
Increase the persistent disk size for all workers to reduce I/O contention.
Why it's wrong here
A is wrong because disk size is rarely the cause of no progress in Dataflow.
- ✓
Examine the worker logs in Cloud Logging for any error messages or exceptions.
Why this is correct
B is correct because logs can reveal the root cause, such as out-of-memory errors or stuck transforms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Force stop the pipeline and update it with a new version using the --update flag.
Why it's wrong here
E is wrong because force stopping may cause data loss; it's better to diagnose without stopping.
- ✓
Enable Dataflow Streaming Engine to move state to the backend and reduce worker load.
Why this is correct
D is correct because Streaming Engine can alleviate progress issues caused by state contention.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing resources (like disk size) or restarting the pipeline is the default fix, when in reality the first step is always to inspect logs to understand the failure mode.
Detailed technical explanation
How to think about this question
Dataflow's 'No progress has been made' message appears when the pipeline's watermark has not advanced for a prolonged period, often due to a stuck transform or a worker that is unresponsive. Under the hood, the Dataflow service monitors the progress of each stage via metrics like 'System Lag' and 'Data Watermark Age'; if these stall, the service logs the warning. A common real-world scenario is a user-defined function (UDF) that throws an exception silently, causing the worker to retry indefinitely without making progress.
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 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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PDE question test?
Building and operationalizing data processing systems — This question tests Building and operationalizing data processing systems — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Examine the worker logs in Cloud Logging for any error messages or exceptions. — Option C is correct because examining worker logs in Cloud Logging is the first step to identify the root cause of a stuck pipeline. Common issues like out-of-memory errors, serialization failures, or worker crashes are logged there, and without inspecting logs, troubleshooting is guesswork.
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.
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Last reviewed: Jun 30, 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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