- A
Use Vertex AI Endpoints with traffic splitting for canary deployments.
Why wrong: Traffic splitting is for gradual rollout, not performance optimization.
- B
Enable autoscaling with a large min replicas count to handle bursts.
Why wrong: This increases cost and does not directly reduce latency per request; it handles scale but not speed.
- C
Optimize the model by quantizing to FP16.
Quantization reduces model size and inference latency, often with minimal accuracy loss.
- D
Use a custom prediction routine with pre-processing inside the container.
Custom prediction routines allow bundling pre-processing steps, reducing network overhead and latency.
- E
Use a machine type with GPU for inference.
Why wrong: GPU may be overkill for many models; not all models benefit, and cost increases. Not a general recommendation.
PMLE Scaling prototypes into ML models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml 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 data science team has trained a custom model using Vertex AI and wants to deploy it for online predictions with low latency. Which TWO actions should they take to optimize 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
Optimize the model by quantizing to FP16.
Option C is correct because quantizing the model to FP16 reduces its memory footprint and computational requirements, directly lowering inference latency on compatible hardware (e.g., NVIDIA GPUs with Tensor Cores). This optimization is especially effective for online predictions where response time is critical, as it accelerates matrix operations without significantly sacrificing model accuracy.
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 Endpoints with traffic splitting for canary deployments.
Why it's wrong here
Traffic splitting is for gradual rollout, not performance optimization.
- ✗
Enable autoscaling with a large min replicas count to handle bursts.
Why it's wrong here
This increases cost and does not directly reduce latency per request; it handles scale but not speed.
- ✓
Optimize the model by quantizing to FP16.
Why this is correct
Quantization reduces model size and inference latency, often with minimal accuracy loss.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a custom prediction routine with pre-processing inside the container.
Why this is correct
Custom prediction routines allow bundling pre-processing steps, reducing network overhead and latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a machine type with GPU for inference.
Why it's wrong here
GPU may be overkill for many models; not all models benefit, and cost increases. Not a general recommendation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that scaling infrastructure (e.g., autoscaling or GPU selection) is the primary way to optimize latency, when in fact model-level changes (quantization) and architectural changes (custom routines) are more direct and cost-effective.
Detailed technical explanation
How to think about this question
FP16 quantization leverages half-precision floating-point format, which halves the model size and allows Tensor Cores to perform more operations per clock cycle. Custom prediction routines (Option D) enable preprocessing (e.g., tokenization, image resizing) to run inside the container, avoiding network round trips to external services and reducing end-to-end latency. In practice, combining quantization with a custom routine can achieve sub-10ms inference times for models like BERT or ResNet 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.
- →
Scaling prototypes into ML models — study guide chapter
Learn the concepts, then practise the questions
- →
Scaling prototypes into ML models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
506 questions across all exam domains
- →
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
- →
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Scaling prototypes into ML models practice questions
Practise PMLE questions linked to Scaling prototypes into ML models.
Automating and orchestrating ML pipelines practice questions
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Collaborating within and across teams to manage data and models practice questions
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Architecting low-code ML solutions practice questions
Practise PMLE questions linked to Architecting low-code ML solutions.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Serving and scaling models practice questions
Practise PMLE questions linked to Serving and scaling models.
Monitoring ML solutions practice questions
Practise PMLE questions linked to Monitoring ML solutions.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Optimize the model by quantizing to FP16. — Option C is correct because quantizing the model to FP16 reduces its memory footprint and computational requirements, directly lowering inference latency on compatible hardware (e.g., NVIDIA GPUs with Tensor Cores). This optimization is especially effective for online predictions where response time is critical, as it accelerates matrix operations without significantly sacrificing model accuracy.
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: Jun 30, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.