- A
The model is too large for the machine type.
Why wrong: Model size could affect loading time but does not explain the improvement after first request.
- B
Cold start occurs because the endpoint scaled down to zero.
Correct. With minReplicas=0, the endpoint scales down to zero, leading to cold start latency.
- C
The VPC Service Controls are blocking the initial request.
Why wrong: VPC Service Controls do not typically cause 30-second delays only on the first request.
- D
The endpoint's autoscaling is misconfigured.
Why wrong: Autoscaling is working correctly; it scaled from 0 to 1.
Question 806 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 deployed a model to a Vertex AI endpoint with minReplicas=0 and maxReplicas=5. After sending prediction requests, you notice the endpoint takes about 30 seconds to respond initially, but subsequent requests are fast. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Cold start occurs because the endpoint scaled down to zero.
Option B is correct because Vertex AI endpoints with minReplicas=0 scale down to zero when idle. The first request after a period of inactivity triggers a cold start, where the endpoint must provision a new VM instance and load the model, causing a ~30-second delay. Subsequent requests are fast because the instance remains warm and handles them without provisioning overhead.
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.
- ✗
The model is too large for the machine type.
Why it's wrong here
Model size could affect loading time but does not explain the improvement after first request.
- ✓
Cold start occurs because the endpoint scaled down to zero.
Why this is correct
Correct. With minReplicas=0, the endpoint scales down to zero, leading to cold start latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The VPC Service Controls are blocking the initial request.
Why it's wrong here
VPC Service Controls do not typically cause 30-second delays only on the first request.
- ✗
The endpoint's autoscaling is misconfigured.
Why it's wrong here
Autoscaling is working correctly; it scaled from 0 to 1.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between cold start latency and persistent performance issues, so candidates may mistakenly attribute the initial delay to model size or network misconfiguration instead of recognizing the intentional scaling-to-zero behavior.
Detailed technical explanation
How to think about this question
Vertex AI endpoints use Knative-based serverless scaling, where minReplicas=0 allows the endpoint to scale to zero after a configurable idle timeout (default ~15 minutes). The cold start involves provisioning a Compute Engine VM, pulling the model container from Artifact Registry, and initializing the model server (e.g., TensorFlow Serving or PyTorch Serve), which can take 20–40 seconds. This behavior is critical for cost optimization in production, where traffic patterns have predictable idle periods, but requires warm-up strategies (e.g., minReplicas=1 or traffic splitting) for latency-sensitive applications.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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
Learn the concepts, then practise the questions
- →
Serving and Scaling Models practice questions
Targeted practice on this topic area only
- →
All PMLE questions
1,000 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.
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.
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.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
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?
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: Cold start occurs because the endpoint scaled down to zero. — Option B is correct because Vertex AI endpoints with minReplicas=0 scale down to zero when idle. The first request after a period of inactivity triggers a cold start, where the endpoint must provision a new VM instance and load the model, causing a ~30-second delay. Subsequent requests are fast because the instance remains warm and handles them without provisioning overhead.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
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.