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.
Your team has deployed a text classification model on Vertex AI Endpoints. You notice that the model's latency has increased significantly over the last week, but the request rate has remained stable. Which of the following is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A change in the preprocessing logic that now includes a computationally expensive step
A computationally expensive preprocessing step directly increases per-request latency on the inference path, even when request rate is stable. Vertex AI Endpoints execute user-provided preprocessing code before model inference, so adding a heavy operation (e.g., large regex, image resizing, or external API call) will linearly increase response time for every prediction.
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.
✗
A sudden increase in the number of prediction requests
Why it's wrong here
Request rate is stable, so not the cause.
✗
The model was replaced with a larger version without updating the endpoint
Why it's wrong here
Model replacement requires redeployment; latency change would be immediate, not gradual.
✓
A change in the preprocessing logic that now includes a computationally expensive step
Why this is correct
This increases per-request latency without changing request rate.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
A misconfiguration in the autoscaling policy
Why it's wrong here
Autoscaling issues typically cause errors under load, not gradual latency increase with stable traffic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'model latency' with 'request rate' and assume any latency increase must be due to scaling issues, ignoring that preprocessing logic changes can dramatically affect per-request performance without altering throughput.
Detailed technical explanation
How to think about this question
Vertex AI Endpoints support custom prediction routines (CPR) where preprocessing logic runs inside the container. If preprocessing involves a heavy operation like a 10MB text embedding lookup or a complex feature transformation, each request incurs that cost before the model inference. Under the hood, Vertex AI measures end-to-end latency from the time the request hits the endpoint to the response, so any preprocessing delay is directly reflected in the observed latency metric. In a real-world scenario, a team might add a spell-checking step that calls an external API, causing latency to spike from 50ms to 500ms without any change in request volume.
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.
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: A change in the preprocessing logic that now includes a computationally expensive step — A computationally expensive preprocessing step directly increases per-request latency on the inference path, even when request rate is stable. Vertex AI Endpoints execute user-provided preprocessing code before model inference, so adding a heavy operation (e.g., large regex, image resizing, or external API call) will linearly increase response time for every prediction.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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