The answer is to set a higher min replicas to maintain a baseline of warm instances. This is correct because autoscaling cold start solutions on Vertex AI hinge on the fact that new instances require several minutes to initialize and load the model, creating latency during traffic spikes. By raising the minimum number of replicas, you ensure a pool of pre-warmed instances is always ready to serve requests instantly, absorbing the initial surge while new instances spin up in the background. On the Google Professional Machine Learning Engineer exam, this question tests your understanding of Vertex AI Prediction’s scaling behavior and the trade-off between cost and latency. A common trap is to assume that reducing the scaling window or enabling faster provisioning solves the cold start problem, but those options do not eliminate the inherent initialization delay. Remember the memory tip: “Warm the pool, don’t cool the spike”—keeping a baseline of warm instances is the only direct way to prevent latency from cold starts.
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.
A company deploys a model on Vertex AI Prediction with autoscaling enabled. They notice that during a traffic spike, new instances take several minutes to become available, causing high latency. What is the best solution?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set a higher min replicas to maintain a baseline of warm instances
Option D is correct because setting a higher min replicas ensures that a baseline number of instances are always warm and ready to serve traffic. During a traffic spike, new instances still take time to provision (cold start), but the warm instances handle the initial surge without latency spikes. This directly addresses the observed high latency during spikes.
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.
✗
Disable autoscaling and use a fixed number of replicas
Why it's wrong here
Fixed replicas may be underutilized or insufficient.
✗
Increase the max replicas setting
Why it's wrong here
Max replicas only caps scaling, does not reduce cold start.
✗
Decrease the machine type to reduce provisioning time
Why it's wrong here
Smaller machines may still have cold start issues.
✓
Set a higher min replicas to maintain a baseline of warm instances
Why this is correct
Warm instances reduce latency during spikes.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that increasing max replicas or decreasing machine type solves cold-start latency, when the real solution is maintaining a warm baseline via min replicas.
Detailed technical explanation
How to think about this question
Vertex AI Prediction uses container-based instances that require pulling the model image and loading the model into memory before serving. This cold-start latency typically ranges from 30 seconds to several minutes, depending on image size and model complexity. Setting a higher min replicas pre-warms instances, so they are already in the 'READY' state and can immediately accept requests, bypassing the cold-start delay. Autoscaling still adds new replicas beyond the minimum as needed, but the warm baseline absorbs the initial spike.
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 — 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: Set a higher min replicas to maintain a baseline of warm instances — Option D is correct because setting a higher min replicas ensures that a baseline number of instances are always warm and ready to serve traffic. During a traffic spike, new instances still take time to provision (cold start), but the warm instances handle the initial surge without latency spikes. This directly addresses the observed high latency during spikes.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team deploys a model using Vertex AI Endpoint with automatic scaling. They observe that during traffic spikes, new instances take a long time to become ready, causing high latency for some requests. What should they configure to reduce this startup time?
medium
A.Increase the max replicas
✓ B.Use a custom container with a smaller footprint
C.Enable predictive autoscaling
D.Set a higher target CPU utilization
Why B: Option D is correct because using a custom container with a smaller footprint (e.g., smaller base image, fewer dependencies) reduces the time to pull and initialize the container. Option A increases max replicas but does not speed up startup. Option B may help trigger scaling earlier but startup time remains. Option C is not a standard setting.
Last reviewed: Jun 30, 2026
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.
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.