The answer is to increase the number of workers via maxNumWorkers. When a streaming Dataflow job shows dataflow streaming parallelism underutilized, the existing workers are not fully loaded, meaning the bottleneck is not per-worker resources but a lack of parallel execution paths. Raising maxNumWorkers directly increases parallelism by allowing the autoscaler to scale out across more VMs, distributing the Pub/Sub backlog and boosting throughput. On the Google Professional Data Engineer exam, this tests your understanding that underutilization signals a need for more workers, not larger ones—a common trap is adjusting machine type or disk size, which won’t fix idle workers. Remember the key distinction: maxNumWorkers controls scale-out parallelism, while worker machine type controls scale-up capacity. For a quick memory tip: “Underutilized? Scale out, not up.”
PDE Designing data processing systems Practice Question
This PDE practice question tests your understanding of designing data processing systems. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 streaming Dataflow job is processing messages from Cloud Pub/Sub. The job is underutilizing resources and the throughput is lower than expected. Which parameter should be adjusted to increase parallelism?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of workers via maxNumWorkers
The job is underutilizing resources, meaning the existing workers are not fully loaded. Increasing the number of workers via maxNumWorkers directly increases parallelism by allowing Dataflow to distribute work across more VMs, which can increase throughput without changing the per-worker resource profile. This parameter controls the upper bound on the number of workers, enabling the autoscaler to scale out when there is 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.
✗
Change the workerMachineType to a higher CPU machine
Why it's wrong here
Better machines improve per-worker throughput but not parallelism.
✓
Increase the number of workers via maxNumWorkers
Why this is correct
More workers allow more parallelism.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set the streaming engine to Dataflow Streaming Engine
Why it's wrong here
This improves latency but not parallelism.
✗
Set autoscalingAlgorithm to THROUGHPUT_BASED
Why it's wrong here
This controls how scaling decisions are made but not directly parallelism.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing per-worker resources (CPU/memory) is the primary way to improve throughput in a streaming job, when in fact underutilization indicates the need to scale out workers rather than scale up individual workers.
Detailed technical explanation
How to think about this question
Dataflow's autoscaler uses a combination of CPU utilization, throughput, and backlog metrics to decide to scale out or in. The maxNumWorkers parameter acts as a hard cap on the number of workers; if this cap is too low, the autoscaler cannot add more workers even if there is significant backlog. In practice, for Pub/Sub streaming jobs, increasing maxNumWorkers allows the autoscaler to create more splits of the subscription's pull partitions, increasing the degree of parallelism in message processing.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Designing data processing systems — This question tests Designing 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 number of workers via maxNumWorkers — The job is underutilizing resources, meaning the existing workers are not fully loaded. Increasing the number of workers via maxNumWorkers directly increases parallelism by allowing Dataflow to distribute work across more VMs, which can increase throughput without changing the per-worker resource profile. This parameter controls the upper bound on the number of workers, enabling the autoscaler to scale out when there is 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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