The answer is to add a secondary worker group with preemptible VMs to scale the Dataflow streaming pipeline horizontally. This is correct because the CPU bottleneck stems from TensorFlow model inference, a CPU-intensive workload that benefits from distributing the load across more workers; preemptible VMs provide a cost-effective way to scale without significantly increasing expenses, as they are up to 80% cheaper than regular instances. On the Google Professional Data Engineer exam, this scenario tests your understanding of horizontal scaling for streaming pipelines with CPU-bound side inputs, where the common trap is to upgrade worker machine types (which increases cost per worker) or adjust streaming engine settings (which doesn’t address CPU saturation). A key memory tip: when you see high CPU utilization and growing SystemLag in a Dataflow pipeline with model inference, think “preemptible workers for cheap horizontal scale”—the bottleneck is compute, not memory or I/O.
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 multinational e-commerce company runs a real-time recommendation system. The architecture: user click events are sent via HTTP to a Cloud Run service, which publishes them to a Cloud Pub/Sub topic. A Dataflow streaming pipeline reads from the subscription, joins with user profile data from Firestore, computes recommendations using a TensorFlow model (loaded as a side input), and writes results to a Redis cache (Memorystore) for low-latency serving. The pipeline is deployed in us-central1. Recently, the team noticed that recommendation latency has increased from 50ms to 500ms, and the pipeline's backlog is growing. The Dataflow monitoring shows high CPU utilization on workers, and the SystemLag metric is 2 minutes and increasing. The Redis cluster shows no performance issues. The Firestore queries are within normal latency. The team suspects the TensorFlow model inference is the bottleneck. The model is a large neural network (500MB) loaded in each worker's memory. The pipeline uses 10 n1-standard-4 workers. The pipeline is using Dataflow's streaming engine. The team wants to reduce latency without increasing cost significantly. What should they do?
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 by adding a secondary worker group with preemptible VMs.
Option A is correct because adding preemptible VMs as a secondary worker group allows horizontal scaling at lower cost, distributing the TensorFlow model inference load across more workers. This reduces CPU utilization per worker and decreases the SystemLag without significantly increasing cost, as preemptible VMs are much cheaper than regular instances. The bottleneck is CPU-bound model inference, not memory, so more workers directly address the high CPU utilization and growing 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.
✓
Increase the number of workers by adding a secondary worker group with preemptible VMs.
Why this is correct
More workers parallelize inference, preemptible VMs keep cost low.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch to a batch pipeline that runs every minute to reduce frequency of inference.
Why it's wrong here
Batch introduces minutes of latency, unacceptable for real-time.
✗
Increase the machine type of workers to n1-highmem-8 to provide more memory for the model.
Why it's wrong here
Higher memory per worker increases cost, but CPU is the bottleneck, not memory.
✗
Remove the model side input and call Cloud Run for inference using a separate service.
Why it's wrong here
Adds network latency and cost, may not scale.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a memory issue (option C) because the model is large, but the real bottleneck is CPU utilization from repeated inference, not memory exhaustion.
Detailed technical explanation
How to think about this question
Dataflow's streaming engine separates compute from storage, but model inference is CPU-intensive and runs on worker VMs. Preemptible VMs can be terminated at any time, so they are best used for stateless, resumable work like inference; Dataflow's autoscaling and checkpointing handle failures gracefully. The SystemLag metric reflects the time from event ingestion to output, and high CPU utilization indicates that inference is the bottleneck, not I/O or memory.
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.
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 by adding a secondary worker group with preemptible VMs. — Option A is correct because adding preemptible VMs as a secondary worker group allows horizontal scaling at lower cost, distributing the TensorFlow model inference load across more workers. This reduces CPU utilization per worker and decreases the SystemLag without significantly increasing cost, as preemptible VMs are much cheaper than regular instances. The bottleneck is CPU-bound model inference, not memory, so more workers directly address the high CPU utilization and growing 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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.