This PMLE practice question tests your understanding of serving and scaling models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 company needs to serve a model with strict latency requirements (<100ms). They are using Vertex AI Prediction with CPU. During testing, latency is 150ms. What should they do?
Enable batching to improve throughput
Why wrong: Batching increases per-request latency.
Use a smaller machine type with more replicas
Why wrong: Smaller machines may be slower.
Export the model to TensorFlow Lite
Why wrong: TensorFlow Lite is for mobile/edge, not Vertex AI.
Switch to a GPU machine type
GPUs can reduce inference latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Switch to a GPU machine type
The model's latency of 150ms exceeds the 100ms requirement. Switching to a GPU machine type (Option D) is correct because GPUs are optimized for parallel computation, significantly reducing inference latency for many ML models, especially deep learning models, compared to CPUs. Vertex AI Prediction supports GPU machine types, and this change directly addresses the latency bottleneck without altering the model or its serving configuration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Enable batching to improve throughput
Why it's wrong here
Batching increases per-request latency.
Use a smaller machine type with more replicas
Why it's wrong here
Smaller machines may be slower.
Export the model to TensorFlow Lite
Why it's wrong here
TensorFlow Lite is for mobile/edge, not Vertex AI.
Switch to a GPU machine type
Why this is correct
GPUs can reduce inference latency.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse throughput optimization (batching or scaling replicas) with latency reduction, failing to recognize that GPUs directly address compute-bound latency while CPU-based solutions cannot meet strict sub-100ms requirements for complex models.
Detailed technical explanation
GPUs leverage thousands of cores for parallel matrix operations, which are fundamental to neural network inference, reducing latency by an order of magnitude for models like transformers or CNNs. In Vertex AI, switching to a GPU machine type (e.g., n1-standard-4 with NVIDIA T4) can drop inference latency from 150ms to under 50ms for many models, while CPU inference is bottlenecked by sequential processing. Real-world scenarios include serving large language models or image classifiers where GPU acceleration is critical for real-time applications like chatbots or fraud detection.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: Switch to a GPU machine type — The model's latency of 150ms exceeds the 100ms requirement. Switching to a GPU machine type (Option D) is correct because GPUs are optimized for parallel computation, significantly reducing inference latency for many ML models, especially deep learning models, compared to CPUs. Vertex AI Prediction supports GPU machine types, and this change directly addresses the latency bottleneck without altering the model or its serving configuration.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This PMLE 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 PMLE exam.
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