The answer is a recent change in the preprocessing code in the container that transformed features differently than what the model expects. This is correct because a systematic precision drop with stable recall and unchanged input distribution points to a feature transformation mismatch, not data drift. When the preprocessing logic in the custom container diverges from the training pipeline, the model receives incorrectly scaled or encoded inputs, causing its probability estimates to shift and degrade precision. On the Google Professional Machine Learning Engineer exam, this scenario tests your ability to distinguish between data drift, model version issues, and preprocessing skew—a common trap is to blame autoscaling or latency, but the increased CPU utilization here signals extra computation from altered preprocessing steps. Memory tip: “Precision plummets, recall stays—check the preprocessing phase.”
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
You are the ML engineer for a financial services company. You have deployed a fraud detection model on Vertex AI Endpoints using a custom container. The model is a gradient boosting model trained on transactional data. Over the past week, the model's precision has dropped from 95% to 80%, while recall has remained stable. The input data volume and distribution have not changed significantly. The model is served on a single endpoint with autoscaling enabled (min replicas=2, max replicas=10). You notice that the average CPU utilization of the serving containers has increased from 40% to 90%, and the p99 latency has increased from 50ms to 200ms. The model is retrained weekly using the latest data, and the last retraining was 3 days ago. The logs show no errors, and the model version is unchanged. Given these symptoms, what is the most likely cause of the precision drop?
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.
The autoscaling policy is not scaling up fast enough, causing increased latency and prediction errors.
Why wrong: Latency increase is a symptom, but precision drop is not caused by scaling issues.
B
The model is overfitting to recent transaction patterns due to weekly retraining.
Why wrong: Overfitting would likely affect both precision and recall.
C
A recent change in the preprocessing code in the container transformed features differently than what the model expects, causing incorrect predictions.
Feature transformation mismatch can cause precision drop without affecting recall.
D
The model was replaced with a different version without updating the endpoint.
Why wrong: Model version is unchanged, so this is not the case.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A recent change in the preprocessing code in the container transformed features differently than what the model expects, causing incorrect predictions.
Option C is correct because the precision drop without a change in input distribution or recall strongly indicates a systematic error in predictions, not a data shift. A preprocessing code change in the custom container would cause the model to receive features transformed differently than during training, leading to incorrect probability estimates. The increased CPU utilization and latency are consistent with the container performing additional or different preprocessing steps, not with autoscaling issues or model version changes.
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 autoscaling policy is not scaling up fast enough, causing increased latency and prediction errors.
Why it's wrong here
Latency increase is a symptom, but precision drop is not caused by scaling issues.
✗
The model is overfitting to recent transaction patterns due to weekly retraining.
Why it's wrong here
Overfitting would likely affect both precision and recall.
✓
A recent change in the preprocessing code in the container transformed features differently than what the model expects, causing incorrect predictions.
Why this is correct
Feature transformation mismatch can cause precision drop without affecting recall.
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 model was replaced with a different version without updating the endpoint.
Why it's wrong here
Model version is unchanged, so this is not the case.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often attribute latency increases and precision drops to autoscaling or model drift, but the key clue is that recall remains stable, which points to a systematic prediction error (preprocessing mismatch) rather than a data distribution shift or infrastructure scaling problem.
Detailed technical explanation
How to think about this question
In Vertex AI custom containers, the preprocessing logic is part of the container image and is executed on every prediction request. If the preprocessing code is modified (e.g., a scaling factor or encoding scheme changes), the model receives feature vectors that are out of distribution relative to its training data, causing miscalibrated probabilities and a drop in precision. The increased CPU utilization (from 40% to 90%) and p99 latency (from 50ms to 200ms) are telltale signs of additional computational overhead, such as new feature transformations or data validation steps, not of scaling or model version issues.
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 recent change in the preprocessing code in the container transformed features differently than what the model expects, causing incorrect predictions. — Option C is correct because the precision drop without a change in input distribution or recall strongly indicates a systematic error in predictions, not a data shift. A preprocessing code change in the custom container would cause the model to receive features transformed differently than during training, leading to incorrect probability estimates. The increased CPU utilization and latency are consistent with the container performing additional or different preprocessing steps, not with autoscaling issues or model version changes.
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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