- A
Use gcloud CLI to prune the model.
Why wrong: gcloud CLI does not have model pruning capabilities; pruning requires specific tools like TensorFlow Model Optimization Toolkit.
- B
Use Cloud TPU for faster inference.
Why wrong: Cloud TPUs are for training, not inference on Vertex AI endpoints (which support GPUs).
- C
Use Vertex AI Model Optimization with TensorRT.
Vertex AI Model Optimization uses TensorRT to quantize and compile models for NVIDIA GPUs, reducing latency and model size.
- D
Use TensorFlow.js converter to optimize the model for web.
Why wrong: TensorFlow.js is for browser deployment, not Vertex AI GPU serving.
Question 686 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.
An ML engineer is optimizing a large model for deployment on Vertex AI with GPU acceleration. They want to reduce model size and improve inference latency without significant accuracy loss. Which tool should they use?
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
✓
Use Vertex AI Model Optimization with TensorRT.
Option C is correct because Vertex AI Model Optimization with TensorRT is specifically designed to reduce model size and improve inference latency on NVIDIA GPUs by applying techniques like quantization, pruning, and graph optimizations. TensorRT optimizes the model for the target GPU architecture, enabling faster inference with minimal accuracy loss, which directly addresses the engineer's goals.
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.
- ✗
Use gcloud CLI to prune the model.
Why it's wrong here
gcloud CLI does not have model pruning capabilities; pruning requires specific tools like TensorFlow Model Optimization Toolkit.
- ✗
Use Cloud TPU for faster inference.
Why it's wrong here
Cloud TPUs are for training, not inference on Vertex AI endpoints (which support GPUs).
- ✓
Use Vertex AI Model Optimization with TensorRT.
Why this is correct
Vertex AI Model Optimization uses TensorRT to quantize and compile models for NVIDIA GPUs, reducing latency and model size.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use TensorFlow.js converter to optimize the model for web.
Why it's wrong here
TensorFlow.js is for browser deployment, not Vertex AI GPU serving.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any optimization tool (like gcloud CLI or TensorFlow.js) can perform model pruning or latency reduction, when in fact each tool has a specific domain—Vertex AI Model Optimization with TensorRT is the only option that directly targets GPU-accelerated inference optimization on Vertex AI.
Detailed technical explanation
How to think about this question
TensorRT works by converting a trained model into an optimized inference engine through layer fusion, precision calibration (e.g., FP16 or INT8 quantization), and kernel auto-tuning for the specific GPU. In practice, this can yield 2-5x latency improvements on NVIDIA GPUs, but requires careful calibration to avoid significant accuracy drops, especially for models with sensitive output distributions like object detection or NLP transformers.
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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Serving and Scaling Models practice questions
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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: Use Vertex AI Model Optimization with TensorRT. — Option C is correct because Vertex AI Model Optimization with TensorRT is specifically designed to reduce model size and improve inference latency on NVIDIA GPUs by applying techniques like quantization, pruning, and graph optimizations. TensorRT optimizes the model for the target GPU architecture, enabling faster inference with minimal accuracy loss, which directly addresses the engineer's goals.
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
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 →
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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