- A
Increase the number of model replicas to the maximum.
Why wrong: More replicas increase cost but may not improve per-request latency; batching is more effective.
- B
Use TensorRT to quantize the model to FP16 or INT8.
Quantization reduces model size and speeds up inference with minimal accuracy loss.
- C
Disable model caching to reduce memory usage.
Why wrong: Disabling caching may increase latency due to model loading time.
- D
Enable dynamic batching in Triton to aggregate requests.
Batching increases throughput by processing multiple requests together.
- E
Use a larger machine type with more vCPUs.
Why wrong: Larger machines increase cost and may not help if the bottleneck is GPU compute.
Question 754 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.
You are optimizing a model for deployment on Vertex AI using NVIDIA Triton Inference Server. Which TWO actions can you take to improve inference performance?
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 TensorRT to quantize the model to FP16 or INT8.
Option B is correct because TensorRT optimizes model inference by quantizing weights and activations to lower precision formats like FP16 or INT8, reducing memory bandwidth and computation time without significant accuracy loss. This is a standard technique for improving throughput on NVIDIA GPUs, especially when deploying with Triton Inference Server, which natively supports TensorRT-optimized model repositories.
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 number of model replicas to the maximum.
Why it's wrong here
More replicas increase cost but may not improve per-request latency; batching is more effective.
- ✓
Use TensorRT to quantize the model to FP16 or INT8.
Why this is correct
Quantization reduces model size and speeds up inference with minimal accuracy loss.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Disable model caching to reduce memory usage.
Why it's wrong here
Disabling caching may increase latency due to model loading time.
- ✓
Enable dynamic batching in Triton to aggregate requests.
Why this is correct
Batching increases throughput by processing multiple requests together.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a larger machine type with more vCPUs.
Why it's wrong here
Larger machines increase cost and may not help if the bottleneck is GPU compute.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that simply adding more replicas or CPU resources will linearly improve inference performance, ignoring the GPU-bound nature of model serving and the importance of batching and precision optimization.
Detailed technical explanation
How to think about this question
TensorRT performs layer fusion and kernel auto-tuning in addition to quantization, which can yield 2-5x speedups for common deep learning models. Dynamic batching (Option D) works by accumulating multiple inference requests into a single batch, maximizing GPU utilization and reducing per-request overhead; Triton's scheduler can be configured with max batch size and delay parameters to balance latency and throughput. In production, combining TensorRT optimization with dynamic batching is a best practice for serving models at scale on Vertex AI.
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.
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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: Use TensorRT to quantize the model to FP16 or INT8. — Option B is correct because TensorRT optimizes model inference by quantizing weights and activations to lower precision formats like FP16 or INT8, reducing memory bandwidth and computation time without significant accuracy loss. This is a standard technique for improving throughput on NVIDIA GPUs, especially when deploying with Triton Inference Server, which natively supports TensorRT-optimized model repositories.
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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