- A
Deploy the model with a GPU machine type and use TensorRT optimisation.
Correct. GPU and TensorRT can improve throughput and latency.
- B
Use Vertex AI Model Optimisation to automatically quantise and compile the model.
Correct. Vertex AI Model Optimisation can optimise the model for the target hardware.
- C
Integrate the model with NVIDIA Triton Inference Server for dynamic batching and model ensembles.
Correct. Triton can optimise inference performance.
- D
Convert the model to TensorFlow Lite and deploy on Vertex AI endpoint.
Why wrong: TFLite is for mobile/edge devices, not cloud endpoints.
- E
Switch to batch prediction to reduce cost.
Why wrong: Batch prediction is for offline jobs, not real-time inference optimisation.
Question 450 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.
Your team is using Vertex AI Prediction for a large-scale NLP model (PyTorch, custom ops). The model currently runs on CPU but you want to optimise inference cost and performance. Which THREE approaches should you consider? (Choose 3)
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 with a GPU machine type and use TensorRT optimisation.
Option A is correct because deploying the model with a GPU machine type (e.g., NVIDIA A100 or T4) and using TensorRT optimization can significantly accelerate inference for PyTorch models with custom ops. TensorRT performs layer fusion, precision calibration (FP16/INT8), and kernel auto-tuning, which reduces latency and improves throughput on GPU hardware. This directly addresses the goal of optimizing both cost and performance for large-scale NLP models.
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 with a GPU machine type and use TensorRT optimisation.
Why this is correct
Correct. GPU and TensorRT can improve throughput and latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Vertex AI Model Optimisation to automatically quantise and compile the model.
Why this is correct
Correct. Vertex AI Model Optimisation can optimise the model for the target hardware.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Integrate the model with NVIDIA Triton Inference Server for dynamic batching and model ensembles.
Why this is correct
Correct. Triton can optimise inference performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Convert the model to TensorFlow Lite and deploy on Vertex AI endpoint.
Why it's wrong here
TFLite is for mobile/edge devices, not cloud endpoints.
- ✗
Switch to batch prediction to reduce cost.
Why it's wrong here
Batch prediction is for offline jobs, not real-time inference optimisation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap in Google PMLE exams is thinking that converting to a lighter framework (like TensorFlow Lite) or switching to batch prediction is a universal optimization, ignoring that custom ops and real-time latency requirements make those approaches invalid for this scenario.
Detailed technical explanation
How to think about this question
Vertex AI Prediction supports custom containers, allowing you to integrate NVIDIA Triton Inference Server, which provides dynamic batching (combining multiple requests into a single GPU kernel launch) and model ensembles (chaining multiple models without intermediate I/O overhead). TensorRT optimization works by converting the model's computational graph into an optimized engine, leveraging INT8 quantization and kernel fusion to reduce memory bandwidth and compute cycles. In practice, combining Triton with TensorRT can yield 2-5x throughput improvements for transformer-based NLP models.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
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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 with a GPU machine type and use TensorRT optimisation. — Option A is correct because deploying the model with a GPU machine type (e.g., NVIDIA A100 or T4) and using TensorRT optimization can significantly accelerate inference for PyTorch models with custom ops. TensorRT performs layer fusion, precision calibration (FP16/INT8), and kernel auto-tuning, which reduces latency and improves throughput on GPU hardware. This directly addresses the goal of optimizing both cost and performance for large-scale NLP models.
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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