The answer is a memory leak in the custom container, making memory monitoring and resource limits the corrective action. This is the most likely root cause because a gradual latency increase over two weeks, as opposed to a sudden spike, points to a slow accumulation of unreleased memory. As the leak grows, the Java or Python garbage collector in the container spends more time reclaiming space, causing longer GC pauses that degrade response times until timeouts occur. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI custom container lifecycle management and the difference between resource exhaustion (gradual) and request spikes (sudden). A common trap is to blame the model itself or network issues, but the steady degradation over days is the hallmark of a memory leak. Memory tip: think “slow creep, not a sudden leap” to distinguish leaks from traffic bursts.
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 e-commerce company uses a recommendation model deployed on Vertex AI Endpoints. The model's latency increases gradually over two weeks, causing timeouts. The model is served using a custom container. What is the most likely root cause and corrective action?
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
✓
The custom container has a memory leak; implement memory monitoring and set container resource limits.
A gradual increase in latency over two weeks, without a sudden spike, strongly indicates a memory leak in the custom container. As the leak accumulates, the container's garbage collection becomes less effective, leading to increased GC pauses and eventual timeouts. Setting resource limits and monitoring memory usage can prevent the container from exhausting host memory and causing performance degradation.
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.
✗
The model is receiving more traffic; scale the number of replicas.
Why it's wrong here
Traffic increase would show a sudden change, not gradual latency increase over two weeks.
✓
The custom container has a memory leak; implement memory monitoring and set container resource limits.
Why this is correct
Memory leaks are a common cause of gradual performance degradation in long-running containers.
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.
✗
The Vertex AI endpoint has changed its URL; update the client application.
Why it's wrong here
The endpoint URL is static and does not change unless explicitly updated.
✗
The model file size has grown due to feature engineering; reduce feature count.
Why it's wrong here
Model file size does not change unless a new model version is deployed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse a gradual latency increase with a traffic scaling issue (Option A), but the slow, steady degradation over weeks is the hallmark of a resource leak, not a demand spike.
Trap categories for this question
Command / output trap
Traffic increase would show a sudden change, not gradual latency increase over two weeks.
Detailed technical explanation
How to think about this question
A memory leak in a custom container causes the JVM or Python process to hold onto objects that are no longer needed, gradually increasing heap usage. As the heap fills, the garbage collector runs more frequently and with longer pause times (GC thrashing), which directly increases request latency. In Vertex AI, custom containers run with default resource limits unless explicitly set; without memory limits, a leak can eventually cause the container to be OOM-killed by the host, leading to timeouts.
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
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
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: The custom container has a memory leak; implement memory monitoring and set container resource limits. — A gradual increase in latency over two weeks, without a sudden spike, strongly indicates a memory leak in the custom container. As the leak accumulates, the container's garbage collection becomes less effective, leading to increased GC pauses and eventual timeouts. Setting resource limits and monitoring memory usage can prevent the container from exhausting host memory and causing performance degradation.
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.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. 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?
hard
A.A sudden increase in the number of prediction requests
B.The model was replaced with a larger version without updating the endpoint
✓ C.A change in the preprocessing logic that now includes a computationally expensive step
D.A misconfiguration in the autoscaling policy
Why C: 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.
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Question Discussion
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