The answer is to enable autoscaling and increase the maximum number of workers. When a Dataflow streaming pipeline suffers from high system lag and backpressure, these two actions directly address the root cause by allowing the service to dynamically allocate more compute resources to handle the increased data volume. Enabling autoscaling lets Dataflow monitor the backlog and automatically add workers, while raising the maximum worker cap ensures the pipeline can scale sufficiently to clear the bottleneck, reducing the end-to-end latency. On the Google Professional Data Engineer exam, this scenario tests your understanding of how Dataflow’s streaming engine manages resource elasticity under load; a common trap is to confuse increasing the parallelism hint with adding workers, but the hint only affects per-worker concurrency, not total capacity. Remember the memory tip: “Scale out, not just up”—when lag spikes, give Dataflow permission to hire more workers, not just ask existing ones to work faster.
PDE Practice Question: Building and operationalizing data processing systems
This PDE practice question tests your understanding of building and operationalizing 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.
Which TWO actions should be taken to optimize a Dataflow streaming pipeline that is experiencing high system lag and backpressure? (Choose two.)
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 worker threads by adjusting the streaming worker's parallelism hint.
Option B is correct because increasing the parallelism hint allows each worker to process more bundles concurrently, which can reduce backpressure by improving throughput without adding more workers. Option C is correct because enabling autoscaling and increasing the maximum number of workers allows the pipeline to dynamically scale out to handle increased load, directly mitigating high system lag and backpressure.
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.
✗
Use a higher memory machine type for all workers.
Why it's wrong here
Memory is not typically the bottleneck in streaming; CPU and threads matter more.
✓
Increase the number of worker threads by adjusting the streaming worker's parallelism hint.
Why this is correct
More threads can increase throughput per worker.
Related concept
Read the scenario before looking for a memorised answer.
✓
Enable autoscaling and increase the maximum number of workers.
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that simply adding more memory or reducing workers will solve backpressure, when in fact the correct approaches involve increasing parallelism or scaling out the worker pool.
Detailed technical explanation
How to think about this question
In Dataflow streaming pipelines, backpressure occurs when downstream stages cannot keep up with upstream data production. The parallelism hint (via --numberOfWorkerHarnessThreads) controls the number of concurrent bundle processing threads per worker, and increasing it can improve CPU utilization. Autoscaling in Dataflow uses the 'autoscalingAlgorithm' set to THROUGHPUT_BASED, which adjusts the number of workers based on the observed lag in the streaming engine; increasing maxNumWorkers allows the service to scale up to handle spikes without hitting a hard cap.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
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 number of worker threads by adjusting the streaming worker's parallelism hint. — Option B is correct because increasing the parallelism hint allows each worker to process more bundles concurrently, which can reduce backpressure by improving throughput without adding more workers. Option C is correct because enabling autoscaling and increasing the maximum number of workers allows the pipeline to dynamically scale out to handle increased load, directly mitigating high system lag and backpressure.
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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Question Discussion
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