hardMultiple ChoiceObjective-mapped
PMLE Practice Question: Your company uses a custom container for model…
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?
⚠ Common exam trap
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.
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
✓
The preprocessing code in the container was updated but the model was not retrained on the new preprocessing
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.
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.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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