- A
Use Vertex AI Model Optimization for automatic compilation.
Why wrong: Model Optimization optimizes the model but does not handle request batching.
- B
Deploy the model with NVIDIA Triton Inference Server configured for dynamic batching.
Correct: Triton supports dynamic batching to improve GPU utilization and reduce per-request latency.
- C
Increase the number of GPU replicas to handle higher concurrency.
Why wrong: Adding replicas increases throughput but does not batch requests; may increase cost without addressing batching need.
- D
Enable model quantization using TensorRT.
Why wrong: Quantization reduces model size and latency but does not batch requests.
Question 676 of 1,000
PMLE Serving and Scaling Models Practice Question
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 uses Vertex AI for online predictions with a large ensemble model that requires GPU acceleration. They want to reduce inference latency by batching multiple requests into a single GPU inference call. What should they configure?
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 NVIDIA Triton Inference Server configured for dynamic batching.
NVIDIA Triton Inference Server supports dynamic batching, which automatically groups multiple inference requests into a single GPU call. This reduces overhead and improves GPU utilization, directly addressing the need to lower latency for online predictions with a large ensemble model on Vertex AI.
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 Vertex AI Model Optimization for automatic compilation.
Why it's wrong here
Model Optimization optimizes the model but does not handle request batching.
- ✓
Deploy the model with NVIDIA Triton Inference Server configured for dynamic batching.
Why this is correct
Correct: Triton supports dynamic batching to improve GPU utilization and reduce per-request latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of GPU replicas to handle higher concurrency.
Why it's wrong here
Adding replicas increases throughput but does not batch requests; may increase cost without addressing batching need.
- ✗
Enable model quantization using TensorRT.
Why it's wrong here
Quantization reduces model size and latency but does not batch requests.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The exam often tests the distinction between model-level optimizations (quantization, compilation) and runtime optimizations (batching), leading candidates to confuse techniques that improve single-request speed with those that improve throughput via request aggregation.
Detailed technical explanation
How to think about this question
Dynamic batching in Triton works by collecting requests over a configurable time window (e.g., 100 microseconds) and then processing them as a batch on the GPU. This is especially effective for ensemble models where multiple sub-models can be batched together, but requires careful tuning of the max batch size and delay to avoid increasing tail latency. In Vertex AI, Triton can be deployed as a custom container, allowing fine-grained control over batching policies.
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 with NVIDIA Triton Inference Server configured for dynamic batching. — NVIDIA Triton Inference Server supports dynamic batching, which automatically groups multiple inference requests into a single GPU call. This reduces overhead and improves GPU utilization, directly addressing the need to lower latency for online predictions with a large ensemble model on Vertex AI.
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 →
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Last reviewed: Jul 4, 2026
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