- A
Set min_replicas to an estimated baseline and max_replicas to a higher number
This ensures always-on capacity for baseline traffic and room to scale.
- B
Set min_replicas and max_replicas equal to a fixed number
Why wrong: Equal replicas removes autoscaling, which may waste resources or not handle spikes.
- C
Set min_replicas to 0 and max_replicas to a high number
Why wrong: min_replicas=0 allows scale-to-zero, causing cold starts and latency spikes.
- D
Do not set min_replicas; let Vertex AI automatically determine
Why wrong: Vertex AI requires explicit min/max settings; there is no automatic determination.
Question 539 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 needs to serve a high-throughput prediction service with strict latency requirements. They want to minimize cold starts and ensure consistent performance. Which endpoint configuration is most appropriate?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Set min_replicas to an estimated baseline and max_replicas to a higher number
Setting min_replicas to an estimated baseline ensures that a minimum number of instances are always running, eliminating cold starts for baseline traffic. Setting max_replicas to a higher number allows the service to scale up to handle traffic spikes while maintaining consistent performance. This configuration balances cost and latency by avoiding the overhead of scaling from zero while still accommodating bursts.
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.
- ✓
Set min_replicas to an estimated baseline and max_replicas to a higher number
Why this is correct
This ensures always-on capacity for baseline traffic and room to scale.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set min_replicas and max_replicas equal to a fixed number
Why it's wrong here
Equal replicas removes autoscaling, which may waste resources or not handle spikes.
- ✗
Set min_replicas to 0 and max_replicas to a high number
Why it's wrong here
min_replicas=0 allows scale-to-zero, causing cold starts and latency spikes.
- ✗
Do not set min_replicas; let Vertex AI automatically determine
Why it's wrong here
Vertex AI requires explicit min/max settings; there is no automatic determination.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that setting min_replicas to 0 is cost-effective, but the trap here is that it ignores the strict latency requirement and the reality of cold start delays in model serving.
Detailed technical explanation
How to think about this question
In Vertex AI Prediction, min_replicas and max_replicas are part of the autoscaling configuration for deployed models. Under the hood, the autoscaler uses metrics like CPU utilization and request latency to adjust the number of replicas, but it cannot scale instantly from zero—cold starts involve loading the model container and initializing the serving binary, which can take several seconds. In real-world scenarios, a common pattern is to set min_replicas to the 25th percentile of expected traffic to absorb baseline load, while max_replicas is set to the 95th percentile to handle spikes without over-provisioning.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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
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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: Set min_replicas to an estimated baseline and max_replicas to a higher number — Setting min_replicas to an estimated baseline ensures that a minimum number of instances are always running, eliminating cold starts for baseline traffic. Setting max_replicas to a higher number allows the service to scale up to handle traffic spikes while maintaining consistent performance. This configuration balances cost and latency by avoiding the overhead of scaling from zero while still accommodating bursts.
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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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