- A
Increase the maximum number of replicas
Why wrong: More replicas improve throughput, not individual request latency.
- B
Deploy the model on a GPU-enabled machine
GPUs accelerate compute-heavy models, reducing latency.
- C
Enable model quantization via Vertex AI Model Optimization
Quantization reduces model size and speeds up inference.
- D
Use a smaller machine type with less memory
Why wrong: Smaller machine may increase latency due to resource constraints.
- E
Enable autoscaling with a lower target CPU utilization
Why wrong: Lower target triggers more replicas, not reduced latency.
Question 277 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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.
A company needs to reduce inference latency for their online prediction service on Vertex AI. Which two actions would help? (Choose 2)
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
✓
Deploy the model on a GPU-enabled machine
Option B is correct because deploying the model on a GPU-enabled machine significantly accelerates matrix operations and parallel computations inherent in deep learning inference, directly reducing per-request latency. Option C is correct because model quantization reduces the precision of model weights (e.g., from FP32 to INT8), which decreases memory footprint and speeds up computation, especially on compatible hardware like TPUs or GPUs.
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 maximum number of replicas
Why it's wrong here
More replicas improve throughput, not individual request latency.
- ✓
Deploy the model on a GPU-enabled machine
Why this is correct
GPUs accelerate compute-heavy models, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable model quantization via Vertex AI Model Optimization
Why this is correct
Quantization reduces model size and speeds up inference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a smaller machine type with less memory
Why it's wrong here
Smaller machine may increase latency due to resource constraints.
- ✗
Enable autoscaling with a lower target CPU utilization
Why it's wrong here
Lower target triggers more replicas, not reduced latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between scaling for throughput (replicas, autoscaling) versus reducing per-request latency (hardware acceleration, model optimization), leading candidates to confuse horizontal scaling with performance optimization.
Detailed technical explanation
How to think about this question
GPU acceleration leverages thousands of CUDA cores for parallel tensor operations, often yielding 10-100x speedups over CPUs for models like BERT or ResNet. Quantization via Vertex AI Model Optimization uses techniques like post-training quantization (PTQ) or quantization-aware training (QAT) to map FP32 values to INT8, reducing model size by ~75% and enabling faster integer arithmetic on specialized hardware. In practice, combining GPU deployment with quantization can reduce latency from hundreds of milliseconds to single-digit milliseconds for real-time applications like fraud detection or recommendation systems.
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 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
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.
- →
Serving and Scaling Models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Deploy the model on a GPU-enabled machine — Option B is correct because deploying the model on a GPU-enabled machine significantly accelerates matrix operations and parallel computations inherent in deep learning inference, directly reducing per-request latency. Option C is correct because model quantization reduces the precision of model weights (e.g., from FP32 to INT8), which decreases memory footprint and speeds up computation, especially on compatible hardware like TPUs or GPUs.
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.
About these practice questions
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Last reviewed: Jul 4, 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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