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PMLE Practice Question: A data science team deploys a custom container on…
A data science team deploys a custom container on Vertex AI Prediction for a PyTorch model. After deployment, the model returns predictions that are consistently off by a constant factor. The model performed correctly during local testing. What is the most likely cause?
⚠ Common exam trap
The trap is assuming a constant-factor error indicates a code bug or version mismatch, when it almost always signals a preprocessing/normalization mismatch between training and serving.
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 serving input function in the container is not applying the same normalization as during training.
A consistent constant-factor error in predictions, with correct local behavior, points to a preprocessing mismatch: the serving input function in the container is not applying the same normalization (e.g., scaling or mean-centering) that was applied during training. Without identical normalization, inputs are systematically shifted, producing outputs off by a constant factor.
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 loaded in evaluation mode, but the training mode was used in testing.
Why it's wrong here
Evaluation mode disables dropout and batch normalisation updates, but those affect variance, not a constant multiplicative offset. A fixed factor points to preprocessing or scaling differences in the serving path. Evaluation mode is genuinely required for deterministic inference, so it would be the answer if predictions varied randomly between runs.
- ✓
The serving input function in the container is not applying the same normalization as during training.
Why this is correct
The container's serving input function must replicate training-time preprocessing exactly. If it omits the same normalisation, inputs reach the PyTorch model on a different scale, producing predictions offset by a constant factor — matching the symptom while local testing, which used preprocessed data, passed.
- ✗
The container is using a different PyTorch version than the training environment.
Why it's wrong here
A differing PyTorch version usually causes loading failures, operator mismatches or deprecation errors, not a uniform scaling of every output. It is tempting because environment drift is a common deployment issue. Version mismatch would be correct if the container crashed or raised runtime exceptions during inference.
- ✗
There is a bug in the custom container's prediction route.
Why it's wrong here
A prediction-route bug would typically corrupt output shape, raise errors or produce erratic values, not a consistent constant factor. It is tempting because the container is the only component changed at deployment. A route bug would be correct if requests failed or returned malformed responses rather than uniformly scaled predictions.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.