- A
Use a regional endpoint to reduce network latency.
Why wrong: While regional endpoints can reduce latency, it does not directly relate to custom metric autoscaling.
- B
Configure the endpoint to use custom metrics from Cloud Monitoring.
Correct. Custom metrics can be used for autoscaling.
- C
Set a target value for the custom metric in the autoscaling policy.
Correct. You need to specify a target metric value for autoscaling to adjust replicas.
- D
Enable GPU acceleration for faster inference.
Why wrong: Not directly related to custom metric autoscaling.
- E
Set minReplicas to 0 to save cost.
Why wrong: Setting minReplicas to 0 may cause cold start, potentially violating latency SLO.
Question 194 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 are deploying a model for real-time inference with strict latency requirements (<100ms P99). You want to autoscale based on custom metrics. Which TWO actions should you take? (Choose 2)
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
✓
Configure the endpoint to use custom metrics from Cloud Monitoring.
Option B is correct because Cloud Monitoring custom metrics allow you to define autoscaling based on signals that are directly relevant to your inference latency, such as request queue depth or model throughput. This enables the autoscaler to react to real-time demand more precisely than CPU or memory utilization alone, which is critical for meeting strict P99 latency targets.
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 a regional endpoint to reduce network latency.
Why it's wrong here
While regional endpoints can reduce latency, it does not directly relate to custom metric autoscaling.
- ✓
Configure the endpoint to use custom metrics from Cloud Monitoring.
Why this is correct
Correct. Custom metrics can be used for autoscaling.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Set a target value for the custom metric in the autoscaling policy.
Why this is correct
Correct. You need to specify a target metric value for autoscaling to adjust replicas.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable GPU acceleration for faster inference.
Why it's wrong here
Not directly related to custom metric autoscaling.
- ✗
Set minReplicas to 0 to save cost.
Why it's wrong here
Setting minReplicas to 0 may cause cold start, potentially violating latency SLO.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google PMLE often tests the distinction between infrastructure-level optimizations (like regional endpoints or GPU acceleration) and autoscaling configuration actions, leading candidates to confuse network latency reduction with scaling metric selection.
Detailed technical explanation
How to think about this question
Custom metrics for autoscaling in Vertex AI are defined via the `metricSpec` in the `AutoscalingMetricSpec` object, where you specify a metric name from Cloud Monitoring and a target value. The autoscaler uses a target-tracking policy that adjusts the number of replicas to keep the metric near the target, using a proportional-integral-derivative (PID) controller to smooth out fluctuations. In practice, a common custom metric is the number of outstanding requests in the model server's queue, which directly correlates with inference latency and allows proactive scaling before latency spikes.
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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Serving and Scaling Models practice questions
Targeted practice on this topic area only
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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: Configure the endpoint to use custom metrics from Cloud Monitoring. — Option B is correct because Cloud Monitoring custom metrics allow you to define autoscaling based on signals that are directly relevant to your inference latency, such as request queue depth or model throughput. This enables the autoscaler to react to real-time demand more precisely than CPU or memory utilization alone, which is critical for meeting strict P99 latency targets.
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
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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