- A
Stop the pipeline, increase the number of workers in the streaming engine configuration, and restart it.
Why wrong: Stopping a streaming pipeline loses checkpointed state and is disruptive; live scaling is preferred.
- B
Increase the batch size in the WriteToBigQuery transform to reduce I/O operations.
Why wrong: Larger batch sizes increase latency and may worsen the backlog if memory is constrained.
- C
Configure a dead-letter queue in Cloud Storage for failed messages to reduce reprocessing load.
Why wrong: Dead-letter queues handle per-request failures, not general backlog growth.
- D
Increase the maximum number of workers in the pipeline's autoscaling configuration to allow more compute resources.
Allowing more workers can reduce backlog if the pipeline is CPU-bound.
- E
Examine the Dataflow monitoring dashboard for metrics like system lag, data freshness, and worker throughput.
Monitoring provides insights into where the bottleneck is.
Quick Answer
The correct actions are to examine the Dataflow monitoring dashboard for metrics like system lag and data freshness, and to increase the maximum number of workers in the autoscaling configuration. These two steps directly address the growing backlog and rising latency by first diagnosing the bottleneck—using system lag to measure how far behind processing is and data freshness to see the delay in output—and then removing the autoscaling cap so Dataflow can provision more workers to handle the increased load. On the Google Professional Data Engineer exam, this scenario tests your understanding of streaming pipeline health metrics and the limits of horizontal scaling; a common trap is to focus on increasing the number of workers without first checking the monitoring dashboard, which can reveal whether the issue is actually a stuck transform or a Pub/Sub subscription lag. Remember the mnemonic “Check then Cap”—always inspect system lag and data freshness before raising the max workers limit to avoid over-provisioning or masking a code defect.
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.
Your team is running a Dataflow streaming pipeline that reads from Pub/Sub, transforms data, and writes to BigQuery. You notice that the pipeline's backlog is growing and the processing latency has increased from seconds to minutes. You need to diagnose and resolve the issue. Which TWO actions should you take? (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
Increase the maximum number of workers in the pipeline's autoscaling configuration to allow more compute resources.
Option D is correct because increasing the maximum number of workers in the autoscaling configuration allows Dataflow to scale out horizontally, adding more compute resources to handle the increased backlog and reduce processing latency. Dataflow's autoscaling algorithm uses metrics like backlog bytes and CPU utilization to decide when to add workers, but it is capped by the max workers setting. Raising this cap enables the pipeline to allocate more VMs, thus processing more messages per second and reducing the backlog.
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.
- ✗
Stop the pipeline, increase the number of workers in the streaming engine configuration, and restart it.
Why it's wrong here
Stopping a streaming pipeline loses checkpointed state and is disruptive; live scaling is preferred.
- ✗
Increase the batch size in the WriteToBigQuery transform to reduce I/O operations.
Why it's wrong here
Larger batch sizes increase latency and may worsen the backlog if memory is constrained.
- ✗
Configure a dead-letter queue in Cloud Storage for failed messages to reduce reprocessing load.
Why it's wrong here
Dead-letter queues handle per-request failures, not general backlog growth.
- ✓
Increase the maximum number of workers in the pipeline's autoscaling configuration to allow more compute resources.
Why this is correct
Allowing more workers can reduce backlog if the pipeline is CPU-bound.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Examine the Dataflow monitoring dashboard for metrics like system lag, data freshness, and worker throughput.
Why this is correct
Monitoring provides insights into where the bottleneck is.
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 you must stop a streaming pipeline to change worker count or that increasing batch size always improves throughput, when in fact Dataflow supports live autoscaling and larger batches can worsen latency.
Detailed technical explanation
How to think about this question
Dataflow's autoscaling algorithm uses the 'backlog bytes' metric (the total unprocessed data in Pub/Sub subscriptions) and 'wall time' to estimate the required number of workers. The max workers setting acts as a hard cap; if the backlog exceeds the capacity of the current max, the pipeline will not scale further, causing latency to increase linearly. In practice, you should also monitor 'system lag' and 'data freshness' in the monitoring dashboard to distinguish between a scaling bottleneck and a slow transform (e.g., a GroupByKey with a large window).
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
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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: Increase the maximum number of workers in the pipeline's autoscaling configuration to allow more compute resources. — Option D is correct because increasing the maximum number of workers in the autoscaling configuration allows Dataflow to scale out horizontally, adding more compute resources to handle the increased backlog and reduce processing latency. Dataflow's autoscaling algorithm uses metrics like backlog bytes and CPU utilization to decide when to add workers, but it is capped by the max workers setting. Raising this cap enables the pipeline to allocate more VMs, thus processing more messages per second and reducing the backlog.
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
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