Quick Answer
The correct metric to configure for a p99 latency alert on Vertex AI real-time prediction models is the p99 latency of prediction requests. This is the right choice because p99 latency represents the worst-case response time experienced by 99% of your requests, meaning only 1% of requests are allowed to exceed the threshold. For a strict SLA of under 100ms, monitoring p99 ensures that even the slowest tail of your traffic remains within bounds, providing a robust guarantee for real-time predictions rather than relying on averages which can hide outliers. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of production monitoring and SLA enforcement for deployed models, often appearing as a distractor against metrics like average latency or request count. A common trap is choosing mean latency, which can appear acceptable even when many requests fail the SLA. Remember the memory tip: “p99 protects the tail, mean can fail.”
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
You need to set up monitoring for a Vertex AI model that serves predictions in real-time. The model is expected to have a latency SLA of under 100ms. Which metric should you configure an alert on to ensure the SLA is met?
p50 latency of prediction requests
Why wrong: Median latency does not capture worst-case.
Prediction drift score
Why wrong: Unrelated to latency SLA.
p99 latency of prediction requests
p99 captures tail latency critical for SLA.
Number of prediction requests per second
Why wrong: Request count does not measure latency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
p99 latency of prediction requests
Option C is correct because p99 latency measures the worst-case latency experienced by 99% of requests, which is the standard metric for enforcing a strict SLA like under 100ms. Monitoring p99 ensures that even the slowest 1% of requests do not violate the threshold, providing a robust guarantee for real-time predictions.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
p50 latency of prediction requests
Why it's wrong here
Median latency does not capture worst-case.
Prediction drift score
Why it's wrong here
Unrelated to latency SLA.
p99 latency of prediction requests
Why this is correct
p99 captures tail latency critical for SLA.
Related concept
Read the scenario before looking for a memorised answer.
Number of prediction requests per second
Why it's wrong here
Request count does not measure latency.
Common exam traps
Google Cloud often tests the misconception that median (p50) latency is sufficient for SLAs, but the trap is that SLAs require tail-latency guarantees (p99 or p999) to catch performance outliers that violate the threshold.
Detailed technical explanation
In Vertex AI, latency monitoring uses the 'aiplatform.googleapis.com/prediction/request_latencies' metric, which is a distribution. Configuring an alert on p99 involves setting a threshold on the 99th percentile of this distribution, typically using Cloud Monitoring's MQL (Monitoring Query Language) with the 'latency' metric and 'p99' alignment. In real-world scenarios, a sudden increase in p99 latency might indicate resource contention (e.g., GPU memory exhaustion) or a model serving issue like a cold start for autoscaling instances.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Monitoring ML solutions — study guide chapter
Learn the concepts, then practise the questions
Monitoring ML solutions practice questions
Targeted practice on this topic area only
All PMLE questions
506 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
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PMLE questions linked to Scaling prototypes into ML models.
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Practise PMLE questions linked to Architecting low-code ML solutions.
Practise PMLE questions linked to Collaborating to manage data and models.
Practise PMLE questions linked to Serving and scaling models.
Practise PMLE questions linked to Monitoring ML solutions.
Practise PMLE questions linked to Solving business challenges with ML.
Practise PMLE questions linked to PMLE fundamentals.
Practise PMLE questions linked to PMLE scenario.
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: p99 latency of prediction requests — Option C is correct because p99 latency measures the worst-case latency experienced by 99% of requests, which is the standard metric for enforcing a strict SLA like under 100ms. Monitoring p99 ensures that even the slowest 1% of requests do not violate the threshold, providing a robust guarantee for real-time predictions.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Jun 30, 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.