easyMultiple Choice
PMLE Practice Question: Using Vertex AI Training to train a model and…
You are using Vertex AI Training to train a model and then automatically deploy the best candidate to a Vertex AI Prediction endpoint via the Vertex AI Model Registry. However, after deployment, you notice that the endpoint returns predictions for the new model, but they are significantly different from the evaluation metrics computed during training. The training scripts used TensorFlow with a serving input function. What is the most likely issue and how would you fix it?
⚠ Common exam trap
PMLE often tests the train/serve skew concept by describing metrics that look good offline but bad online, tempting candidates to blame infrastructure (machine type) or versioning (alias) instead of the preprocessing mismatch.
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's preprocessing steps do not match the training preprocessing; you should verify and align them.
The classic cause of a train/serve skew in Vertex AI is that the serving input function applies different preprocessing than the training pipeline — for example, different tokenization, normalization, or feature scaling. TensorFlow's serving_input_fn defines the graph that runs at prediction time, so any divergence from the training input_fn produces predictions that look correct structurally but are numerically off. Aligning the two preprocessing paths (ideally by sharing a single feature-engineering module) resolves the discrepancy.
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 endpoint is using a different machine type affecting numerical precision; you should use the same machine type as training.
Why it's wrong here
Machine type alters throughput and memory, not floating-point results; TensorFlow serving uses the same numeric precision across Vertex AI prediction machines. Matching machine types matters for reproducing training throughput or memory limits, not for reconciling offline and online prediction values.
- ✓
The serving input function's preprocessing steps do not match the training preprocessing; you should verify and align them.
Why this is correct
Training metrics are computed on preprocessed features, but the serving input function applies its own transformations. If those steps diverge, the endpoint receives differently scaled or encoded inputs, producing skewed predictions. Aligning the serving preprocessing with training preprocessing restores consistency.
- ✗
The model registry deployed a different version; you should check the alias.
Why it's wrong here
The stem states the endpoint already serves the new model's predictions, so alias resolution is not selecting a stale version. Alias checks belong to staged rollout workflows, where a named alias points to a candidate version you intend to promote or roll back.
- ✗
The model was saved with training-only metrics; you should retrain with evaluation metrics.
Why it's wrong here
Metrics are computed by evaluation code and never embedded in the SavedModel, so retraining cannot change serving outputs. Retraining for metric reasons applies when evaluation itself is flawed, such as a mis-specified split or label leakage in the evaluation pipeline.
Visual reference
Go deeper
Related to this question
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
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.