The answer is Vertex AI Prediction with autoscaling and GPU machine types. This architecture is correct because GPUs dramatically accelerate deep neural network inference, enabling the sub-100ms latency required for real-time fraud detection, while autoscaling dynamically adjusts compute resources to sustain 1000 predictions per second without over-provisioning or cold-start delays. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of balancing latency, throughput, and cost—a common trap is choosing Cloud Functions or Cloud Run, which lack native GPU support for heavy inference. Remember the key pairing: for low-latency high throughput Vertex AI GPU autoscaling, always match GPU-accelerated serving with horizontal scaling. A useful mnemonic is “GPU + Auto = Low Latency, High Throughput,” reinforcing that the GPU handles compute speed while autoscaling handles volume spikes.
PMLE Solving business challenges with ML Practice Question
This PMLE practice question tests your understanding of solving business challenges with ml. 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 financial institution needs to deploy a fraud detection model with strict latency <100ms per prediction and high throughput (1000 predictions/sec). The model is a deep neural network. Which architecture on Google Cloud meets these requirements?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Vertex AI Prediction with autoscaling enabled and GPU machine types
Vertex AI Prediction with autoscaling and GPU machine types is correct because it provides low-latency online serving with autoscaling to handle high throughput (1000 predictions/sec) while keeping latency under 100ms. GPUs accelerate deep neural network inference, and autoscaling ensures resources match demand without over-provisioning.
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.
✗
Deploy the model on AI Platform Training with a single large VM
Why it's wrong here
AI Platform Training is for training, not serving.
✗
Deploy the model as a Cloud Function triggered by Cloud Pub/Sub
Why it's wrong here
Cloud Functions are not designed for high-throughput real-time inference.
✗
Use Vertex AI Batch Prediction with a fixed number of machines
Why it's wrong here
Batch prediction is not real-time; latency is high.
✓
Use Vertex AI Prediction with autoscaling enabled and GPU machine types
Why this is correct
Vertex AI Prediction provides real-time endpoints with autoscaling and GPU support for low latency and high throughput.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between batch and online prediction services, where candidates mistakenly choose batch prediction for real-time requirements because they focus on throughput without considering latency constraints.
Detailed technical explanation
How to think about this question
Vertex AI Prediction uses NVIDIA GPUs (e.g., T4, V100) with TensorFlow Serving or custom containers to accelerate inference via CUDA and cuDNN libraries. Autoscaling is based on CPU/GPU utilization metrics, scaling from zero to hundreds of nodes, and integrates with Cloud Load Balancing for request distribution. In practice, a model with 10ms GPU inference time can handle 1000 QPS with 10 replicas, but cold starts from zero can cause initial latency spikes, so a minimum replica count is recommended.
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.
Solving business challenges with ML — This question tests Solving business challenges with ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Vertex AI Prediction with autoscaling enabled and GPU machine types — Vertex AI Prediction with autoscaling and GPU machine types is correct because it provides low-latency online serving with autoscaling to handle high throughput (1000 predictions/sec) while keeping latency under 100ms. GPUs accelerate deep neural network inference, and autoscaling ensures resources match demand without over-provisioning.
What should I do if I get this PMLE 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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