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.
fetch ml.googleapis.com/prediction_latencies | filter resource.model_id = "my_model" | every 1m | mean
Refer to the exhibit. What does this query return?
fetch ml.googleapis.com/prediction_latencies | filter resource.model_id = "my_model" | every 1m | mean
The maximum latency per minute
Why wrong: It uses 'mean', not 'max'.
The error rate per minute
Why wrong: The metric is latency, not errors.
The total number of predictions per minute
Why wrong: No count function is used.
The average latency per minute for the model
The query uses 'mean' aggregator over 1-minute windows.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The average latency per minute for the model
The query uses the `rate` function to calculate the per-second rate of increase of the `latency_seconds` counter, and then applies the `avg` aggregator to compute the average latency across all instances over the specified time range. The `by (model)` clause groups the result by the `model` label, so the output is the average latency per minute for each model. This is why option D is correct.
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.
The maximum latency per minute
Why it's wrong here
It uses 'mean', not 'max'.
The error rate per minute
Why it's wrong here
The metric is latency, not errors.
The total number of predictions per minute
Why it's wrong here
No count function is used.
The average latency per minute for the model
Why this is correct
The query uses 'mean' aggregator over 1-minute windows.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Google Cloud often tests the distinction between `avg` and `max` aggregators in PromQL queries, and candidates mistakenly think `rate` alone implies a maximum or total, rather than understanding that `avg` computes the mean over the rate values.
Detailed technical explanation
In PromQL, the `rate` function calculates the per-second average rate of increase of a counter over a specified time window (here, `[1m]`). The `avg` aggregator then computes the arithmetic mean of the resulting rate values across all time series, grouped by the `model` label. This is commonly used in monitoring ML model serving latency to understand average performance, but note that `rate` on a gauge-like metric (if `latency_seconds` were a gauge) would be incorrect; however, in this context, `latency_seconds` is typically a counter (e.g., cumulative latency), making `rate` appropriate for deriving a per-second average latency rate.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: The average latency per minute for the model — The query uses the `rate` function to calculate the per-second rate of increase of the `latency_seconds` counter, and then applies the `avg` aggregator to compute the average latency across all instances over the specified time range. The `by (model)` clause groups the result by the `model` label, so the output is the average latency per minute for each model. This is why option D is correct.
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.
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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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