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 gaming company uses Pub/Sub to ingest player events and Dataflow for real-time analytics. They notice that the Pub/Sub subscription backlog is growing despite the Dataflow pipeline running continuously. The pipeline has a 1-hour window for aggregations. What is the most effective way to reduce the backlog?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the Dataflow pipeline's worker count via autoscaling.
Increasing the Dataflow pipeline's worker count via autoscaling directly addresses the backlog by adding more parallel processing capacity to consume messages from the Pub/Sub subscription faster. Since the pipeline is continuously running but the backlog grows, the bottleneck is processing throughput, not pipeline availability. Autoscaling allows Dataflow to dynamically allocate more workers based on the backlog size, matching consumption rate to the incoming message rate.
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.
✓
Increase the Dataflow pipeline's worker count via autoscaling.
Why this is correct
More workers increase parallelism and processing rate, reducing backlog.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a push subscription instead of pull.
Why it's wrong here
Delivery mode does not affect pipeline processing speed.
✗
Decrease the window duration to 10 minutes.
Why it's wrong here
Smaller windows reduce latency but do not increase overall throughput.
✗
Enable Pub/Sub topic retention.
Why it's wrong here
Retention only stores messages longer, does not reduce backlog.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that changing window duration or subscription type can fix a throughput bottleneck, when the real solution is scaling compute resources to match the consumption rate.
Detailed technical explanation
How to think about this question
Dataflow autoscaling uses the 'autoscalingAlgorithm' parameter (e.g., THROUGHPUT_BASED) to adjust the number of workers based on the 'current backlog' metric from Pub/Sub. The backlog is measured as the total number of unacknowledged messages across all subscriptions, and Dataflow's streaming engine uses checkpointing to track progress. In real-world scenarios, if the pipeline has a 1-hour window, the backlog can spike due to late-arriving data or sudden traffic bursts, and autoscaling helps by scaling up workers within the project's quota limits.
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 Dataflow pipeline's worker count via autoscaling. — Increasing the Dataflow pipeline's worker count via autoscaling directly addresses the backlog by adding more parallel processing capacity to consume messages from the Pub/Sub subscription faster. Since the pipeline is continuously running but the backlog grows, the bottleneck is processing throughput, not pipeline availability. Autoscaling allows Dataflow to dynamically allocate more workers based on the backlog size, matching consumption rate to the incoming message rate.
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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