- A
Instrument the prediction container to emit custom metrics for the time spent in each prediction step, including the external API call.
Custom metrics provide granular breakdown.
- B
Add a timeout setting to the endpoint's request to limit the external API call duration.
Why wrong: Doesn't help monitor the contribution.
- C
Monitor the Vertex AI endpoint latency metric and correlate with system metrics like CPU and memory.
Why wrong: Cannot isolate external API latency from system metrics.
- D
Use Cloud Trace to trace the prediction request end-to-end, including the external API call.
Why wrong: Cloud Trace works for services directly integrated with GCP, but external API calls need manual instrumentation; this is more complex and less direct than custom metrics.
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.
A company uses Vertex AI Predictions with a custom container that invokes an external API for feature enrichment. The prediction response time is highly variable. The engineer wants to monitor the external API's contribution to latency. What should the engineer do?
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
Instrument the prediction container to emit custom metrics for the time spent in each prediction step, including the external API call.
Option A is correct because instrumenting the custom container to emit custom metrics (e.g., using OpenTelemetry or a Prometheus client library) allows the engineer to directly measure the time spent in each prediction step, isolating the external API call's contribution to latency. This provides granular, real-time visibility into the specific bottleneck, which is essential when the response time is highly variable and the external API is a known dependency.
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.
- ✓
Instrument the prediction container to emit custom metrics for the time spent in each prediction step, including the external API call.
Why this is correct
Custom metrics provide granular breakdown.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add a timeout setting to the endpoint's request to limit the external API call duration.
Why it's wrong here
Doesn't help monitor the contribution.
- ✗
Monitor the Vertex AI endpoint latency metric and correlate with system metrics like CPU and memory.
Why it's wrong here
Cannot isolate external API latency from system metrics.
- ✗
Use Cloud Trace to trace the prediction request end-to-end, including the external API call.
Why it's wrong here
Cloud Trace works for services directly integrated with GCP, but external API calls need manual instrumentation; this is more complex and less direct than custom metrics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the distinction between monitoring (custom metrics) and tracing (Cloud Trace) — the trap here is that candidates assume Cloud Trace automatically captures all downstream calls, but it requires explicit instrumentation of the external API call to record its duration, whereas custom metrics can be emitted directly from the container code without needing distributed tracing context.
Detailed technical explanation
How to think about this question
Custom metrics emitted from within the container can be collected via Vertex AI's built-in integration with Cloud Monitoring, allowing the engineer to create a histogram of the external API call duration. This approach leverages the OpenTelemetry SDK to create spans for each step, which can be exported as both metrics and traces, but the key advantage for latency monitoring is the ability to set up alerts on the p99 latency of the external call. In a real-world scenario, if the external API occasionally spikes to 10 seconds, the custom metric will capture that, whereas endpoint-level metrics would only show the overall increase without attribution.
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.
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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: Instrument the prediction container to emit custom metrics for the time spent in each prediction step, including the external API call. — Option A is correct because instrumenting the custom container to emit custom metrics (e.g., using OpenTelemetry or a Prometheus client library) allows the engineer to directly measure the time spent in each prediction step, isolating the external API call's contribution to latency. This provides granular, real-time visibility into the specific bottleneck, which is essential when the response time is highly variable and the external API is a known dependency.
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: 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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