The answer is a preprocessing mismatch in the Vertex AI custom container, where the inference preprocessing code was updated but the model was not retrained on the new logic. This is correct because any change to scaling, normalization, or feature engineering in the container directly alters the input tensors sent to the model, causing out-of-distribution inputs that yield nonsensical outputs like negative probabilities—even though no runtime errors occur. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of the critical symmetry between training and serving pipelines; a common trap is assuming that error-free logs mean correct predictions. Remember the memory tip: “Train what you serve, serve what you train”—if preprocessing changes, the model must be retrained on the transformed data to maintain input distribution alignment.
PMLE Monitoring ML solutions Practice Question
This PMLE practice question tests your understanding of monitoring ml solutions. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 company uses a custom container for model serving on Vertex AI. After a recent update, the model returns predictions but they are clearly wrong (e.g., negative probabilities for a classification model). The logs show no errors. What 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
✓
The preprocessing code in the container was updated but the model was not retrained on the new preprocessing
Option A is correct because the most likely cause of a model returning predictions without errors, but with clearly wrong outputs like negative probabilities, is a mismatch between the preprocessing logic used during training and inference. If the preprocessing code in the container was updated (e.g., scaling, normalization, or feature engineering steps changed) but the model was not retrained on data processed with that new logic, the model receives inputs that are out of distribution, leading to nonsensical outputs. Vertex AI containers run inference with the deployed code, so any change in preprocessing directly affects the input tensor values without raising runtime errors.
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 preprocessing code in the container was updated but the model was not retrained on the new preprocessing
Why this is correct
Feature transformation mismatch leads to incorrect predictions.
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 file is corrupted
Why it's wrong here
Would likely cause loading errors.
✗
The model file was accidentally replaced with a different model
Why it's wrong here
Would likely cause different but not necessarily negative probabilities.
✗
The container is using an incompatible version of the serving framework
Why it's wrong here
Would likely cause errors or crashes, not silent wrong predictions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the concept that silent prediction errors (no logs, no crashes) are almost always due to data or preprocessing mismatches, not infrastructure or model file issues, which would generate explicit errors.
Detailed technical explanation
How to think about this question
In classification models, probabilities are typically produced by a softmax or sigmoid activation in the final layer, which constrains outputs to [0,1]. Negative probabilities indicate that the model received input features that fall outside the range or distribution it was trained on, causing the logits to be extreme and the activation function to behave unexpectedly (e.g., due to floating-point underflow or overflow). This is a classic 'training-serving skew' scenario where the inference pipeline applies transformations (e.g., min-max scaling, log transforms) that differ from those used during training, often because the preprocessing code was updated independently of the model artifact.
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: The preprocessing code in the container was updated but the model was not retrained on the new preprocessing — Option A is correct because the most likely cause of a model returning predictions without errors, but with clearly wrong outputs like negative probabilities, is a mismatch between the preprocessing logic used during training and inference. If the preprocessing code in the container was updated (e.g., scaling, normalization, or feature engineering steps changed) but the model was not retrained on data processed with that new logic, the model receives inputs that are out of distribution, leading to nonsensical outputs. Vertex AI containers run inference with the deployed code, so any change in preprocessing directly affects the input tensor values without raising runtime errors.
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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