- A
The 99th percentile of the `prediction/online/response_latencies` metric.
This metric provides quantile data for latency, allowing you to monitor the 99th percentile.
- B
The number of prediction requests that timeout.
Why wrong: Timeouts are a different indicator; they may not capture latency issues below timeout threshold.
- C
Average prediction latency from the endpoint's logs.
Why wrong: Average latency does not capture tail latency (99th percentile).
- D
The maximum prediction latency from the endpoint's monitoring dashboard.
Why wrong: Maximum latency is outlier-prone and not a reliable SLO metric.
Question 856 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.
Your team has deployed a model on Vertex AI endpoints. You need to monitor the prediction latency to ensure it meets a 99th percentile SLO of 500ms. You want to set up an alert if the latency exceeds this threshold. Which metric should you use?
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
✓
The 99th percentile of the `prediction/online/response_latencies` metric.
Option A is correct because the `prediction/online/response_latencies` metric in Vertex AI provides a distribution of latency values, allowing you to query the 99th percentile directly. This aligns with the SLO requirement to monitor the tail latency, not the average or maximum, ensuring that the worst-case performance for 1% of requests stays under 500ms.
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 99th percentile of the `prediction/online/response_latencies` metric.
Why this is correct
This metric provides quantile data for latency, allowing you to monitor the 99th percentile.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The number of prediction requests that timeout.
Why it's wrong here
Timeouts are a different indicator; they may not capture latency issues below timeout threshold.
- ✗
Average prediction latency from the endpoint's logs.
Why it's wrong here
Average latency does not capture tail latency (99th percentile).
- ✗
The maximum prediction latency from the endpoint's monitoring dashboard.
Why it's wrong here
Maximum latency is outlier-prone and not a reliable SLO metric.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between tail latency (percentiles) and central tendency (average) or extreme values (maximum), trapping candidates who confuse SLO monitoring with simple failure counts or averages.
Detailed technical explanation
How to think about this question
Vertex AI endpoints expose the `prediction/online/response_latencies` metric as a distribution (e.g., using a histogram), which is sampled and aggregated by Cloud Monitoring. The 99th percentile is calculated from this distribution, not from raw logs, and can be queried using MQL (Monitoring Query Language) with the `fetch` and `metric` commands. In practice, a sudden increase in the 99th percentile might indicate a resource bottleneck (e.g., CPU or memory pressure) or a change in request payload size, requiring investigation beyond simple threshold alerts.
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.
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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: The 99th percentile of the `prediction/online/response_latencies` metric. — Option A is correct because the `prediction/online/response_latencies` metric in Vertex AI provides a distribution of latency values, allowing you to query the 99th percentile directly. This aligns with the SLO requirement to monitor the tail latency, not the average or maximum, ensuring that the worst-case performance for 1% of requests stays under 500ms.
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
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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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