- A
Create a custom metric from the prediction container that emits latency percentiles, then set an alert on that metric.
Why wrong: Unnecessary effort since Vertex AI already exposes latency metrics.
- B
Use the 'aiplatform.googleapis.com/prediction/online_prediction_latencies' metric with a metric threshold condition set to 500ms and a percentile aligner of 99.
This directly monitors the 99th percentile latency.
- C
Use a log-based metric to parse latency from Cloud Logging and alert when the average exceeds 500ms.
Why wrong: Average is not percentile and log-based metrics are less efficient.
- D
Export prediction latency logs to BigQuery and run a scheduled query to check the 99th percentile, then trigger a Cloud Function to send an alert.
Why wrong: Less direct and real-time than Cloud Monitoring alerts.
Quick Answer
The correct approach is to use the `aiplatform.googleapis.com/prediction/online_prediction_latencies` metric with a metric threshold condition of 500ms and a percentile aligner of 99. This works because Cloud Monitoring natively supports percentile alignment on its pre-built Vertex AI metrics, allowing you to directly measure the 99th percentile latency without custom instrumentation or log-based processing. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Cloud Monitoring’s metric threshold alerts and the distinction between using pre-built metrics versus custom metrics or external tools—a common trap is to overcomplicate the solution with custom code or third-party services when the native metric already provides the exact data needed. Remember that the key is to align on the percentile before setting the threshold, not the other way around. A useful memory tip: think “99 align, then 500 threshold” to keep the order straight.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
An ML team is using Vertex AI Online Prediction and wants to receive alerts when the 99th percentile latency exceeds 500ms for more than 5 minutes. What is the best practice to set up this alert in Cloud Monitoring?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Use the 'aiplatform.googleapis.com/prediction/online_prediction_latencies' metric with a metric threshold condition set to 500ms and a percentile aligner of 99.
Option B is correct because Cloud Monitoring provides a pre-built metric, `aiplatform.googleapis.com/prediction/online_prediction_latencies`, which directly captures prediction latency. By applying a percentile aligner of 99 and a metric threshold condition of 500ms, you can alert when the 99th percentile latency exceeds 500ms for the specified duration, without needing custom instrumentation or external processing.
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.
- ✗
Create a custom metric from the prediction container that emits latency percentiles, then set an alert on that metric.
Why it's wrong here
Unnecessary effort since Vertex AI already exposes latency metrics.
- ✓
Use the 'aiplatform.googleapis.com/prediction/online_prediction_latencies' metric with a metric threshold condition set to 500ms and a percentile aligner of 99.
Why this is correct
This directly monitors the 99th percentile latency.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a log-based metric to parse latency from Cloud Logging and alert when the average exceeds 500ms.
Why it's wrong here
Average is not percentile and log-based metrics are less efficient.
- ✗
Export prediction latency logs to BigQuery and run a scheduled query to check the 99th percentile, then trigger a Cloud Function to send an alert.
Why it's wrong here
Less direct and real-time than Cloud Monitoring alerts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that you must create custom metrics or use log-based solutions for percentile-based alerting, when in fact Cloud Monitoring's distribution metrics and percentile aligners handle this natively.
Detailed technical explanation
How to think about this question
The `aiplatform.googleapis.com/prediction/online_prediction_latencies` metric is a distribution metric that automatically captures latency percentiles (e.g., 50th, 90th, 99th) via the Cloud Monitoring API. When you set a percentile aligner to 99, the alerting system computes the 99th percentile over the alignment window (e.g., 5 minutes) and compares it to the threshold, ensuring you detect sustained high-latency events rather than transient spikes. This approach leverages Google Cloud's native observability stack, avoiding the overhead of custom metric emission or external data pipelines.
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?
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the 'aiplatform.googleapis.com/prediction/online_prediction_latencies' metric with a metric threshold condition set to 500ms and a percentile aligner of 99. — Option B is correct because Cloud Monitoring provides a pre-built metric, `aiplatform.googleapis.com/prediction/online_prediction_latencies`, which directly captures prediction latency. By applying a percentile aligner of 99 and a metric threshold condition of 500ms, you can alert when the 99th percentile latency exceeds 500ms for the specified duration, without needing custom instrumentation or external processing.
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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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: 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.
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