- A
Upgrade the machine type to one with more GPU memory (e.g., n1-standard-8 with a larger GPU) to reduce model inference time.
Why wrong: GPU memory is not the issue; GPU utilization is low, so inference is fast. More GPU memory won't help CPU bottleneck.
- B
Change the batch size in the model serving code to process multiple requests together, improving GPU utilization.
Why wrong: Batching increases latency for individual requests as they wait for a batch to fill. It would not reduce CPU bottleneck and could hurt latency SLO.
- C
Increase the number of replicas (nodes) to parallelize the CPU-bound preprocessing work.
Adding more nodes will distribute the preprocessing load across multiple CPUs, reducing the overall latency per request if the load balancer dispatches requests efficiently. However, this increases cost.
- D
Offload preprocessing to a dedicated Cloud Run service that runs asynchronously and returns precomputed feature vectors.
Why wrong: Asynchronous preprocessing would not help if the model needs features inline; synchronous processing is required. Offloading could add network latency.
Quick Answer
The answer is to increase the number of replicas (nodes) to parallelize the CPU-bound preprocessing work. This is correct because the high CPU utilization at 90% with low GPU utilization below 20% clearly indicates a CPU preprocessing bottleneck, not a GPU inference limitation; the new model’s larger size has shifted the workload to CPU-intensive feature transformations, so distributing requests across multiple replicas reduces per-node CPU pressure and lowers latency without altering model accuracy. On the Google Professional Data Engineer exam, this scenario tests your ability to diagnose scaling bottlenecks by reading utilization metrics—a common trap is to assume a larger model always needs a bigger GPU, but the real fix is horizontal scaling for CPU-bound preprocessing. Remember the memory tip: when GPU is idle but CPU is maxed, think “preprocessing is the bottleneck—scale out, not up.”
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning models. 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.
Your company runs a real-time recommendation system for a popular e-commerce website using a machine learning model deployed on Vertex AI Endpoints. The model takes user features and product catalog data as input and returns top-10 product recommendations. The system uses a feature store to serve user embeddings and product embeddings. Recently, the recommender team retrained the model with a new algorithm and deployed it as a new version. Since the deployment, the latency for recommendation requests has increased from 100ms to 500ms on average, exceeding the 200ms SLO. The model accuracy is acceptable, and there are no errors. The endpoint uses an n1-standard-8 machine with a single GPU. The new model is larger but still fits on the GPU. You investigate and find that the GPU utilization remains low (<20%), but CPU utilization is high (90%). What should you do to reduce latency while maintaining accuracy?
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 number of replicas (nodes) to parallelize the CPU-bound preprocessing work.
Option C is correct because the high CPU utilization (90%) with low GPU utilization (<20%) indicates that the bottleneck is CPU-bound preprocessing, not GPU inference. Increasing the number of replicas (nodes) distributes the CPU preprocessing load across multiple instances, reducing per-request latency without affecting model accuracy. This directly addresses the root cause while keeping the existing GPU resources.
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.
- ✗
Upgrade the machine type to one with more GPU memory (e.g., n1-standard-8 with a larger GPU) to reduce model inference time.
Why it's wrong here
GPU memory is not the issue; GPU utilization is low, so inference is fast. More GPU memory won't help CPU bottleneck.
- ✗
Change the batch size in the model serving code to process multiple requests together, improving GPU utilization.
Why it's wrong here
Batching increases latency for individual requests as they wait for a batch to fill. It would not reduce CPU bottleneck and could hurt latency SLO.
- ✓
Increase the number of replicas (nodes) to parallelize the CPU-bound preprocessing work.
Why this is correct
Adding more nodes will distribute the preprocessing load across multiple CPUs, reducing the overall latency per request if the load balancer dispatches requests efficiently. However, this increases cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Offload preprocessing to a dedicated Cloud Run service that runs asynchronously and returns precomputed feature vectors.
Why it's wrong here
Asynchronous preprocessing would not help if the model needs features inline; synchronous processing is required. Offloading could add network latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that GPU utilization must be increased to reduce latency, but the trap here is that the bottleneck is CPU-bound preprocessing, not GPU inference, so scaling replicas (horizontal scaling) is the correct fix, not GPU upgrades or batching.
Detailed technical explanation
How to think about this question
In Vertex AI Endpoints, each replica runs a model server (e.g., TensorFlow Serving or TorchServe) that handles both preprocessing (e.g., feature transformations, embedding lookups) and GPU inference. When CPU utilization is high but GPU is idle, the CPU is the bottleneck—often due to Python GIL, serialization, or feature engineering. Increasing replicas horizontally scales the CPU work, allowing each request to be processed on a less loaded CPU, while the GPU remains underutilized but sufficient for inference. Real-world scenarios include models with complex feature engineering or large embedding lookups that saturate CPU cores.
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
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FAQ
Questions learners often ask
What does this PDE question test?
Operationalizing machine learning models — This question tests Operationalizing machine learning models — 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 replicas (nodes) to parallelize the CPU-bound preprocessing work. — Option C is correct because the high CPU utilization (90%) with low GPU utilization (<20%) indicates that the bottleneck is CPU-bound preprocessing, not GPU inference. Increasing the number of replicas (nodes) distributes the CPU preprocessing load across multiple instances, reducing per-request latency without affecting model accuracy. This directly addresses the root cause while keeping the existing GPU resources.
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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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 →
Same concept, more angles
1 more ways this is tested on PDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You deployed a model on Vertex AI Endpoints using a custom container. The model serves predictions but the latency is higher than expected. You suspect the container is not making full use of the CPU resources. What should you do to reduce latency?
medium- ✓ A.Modify the container to use multi-threading or increase the number of workers in the prediction server (e.g., Gunicorn workers).
- B.Enable response caching on the endpoint.
- C.Change the machine type to a GPU-accelerated machine.
- D.Increase the number of nodes by adjusting autoscaling limits.
Why A: Option A is correct because high latency in a CPU-based custom container often stems from underutilizing available CPU cores. By increasing the number of workers (e.g., Gunicorn workers) or enabling multi-threading, you allow the prediction server to handle multiple requests concurrently, reducing queue time and improving throughput. This directly addresses the symptom of the container not making full use of CPU resources.
Last reviewed: Jun 30, 2026
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.
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