PMLE Scaling Prototypes into ML Models Practice Question
You want to use a pre-trained model from TensorFlow Hub for image classification, but you need to adapt it to classify your own custom categories with a small dataset. Which Vertex AI approach is most appropriate?
⚠ Common exam trap
The trap is confusing deployment with adaptation. Candidates may think that deploying a pre-trained model via JumpStart or a custom container will somehow adapt it to new categories, but adaptation requires training/fine-tuning.
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
✓
Write a custom training script that loads the pre-trained model and fine-tunes it on your dataset
Fine-tuning a pre-trained model on your custom dataset is the most appropriate approach when you have a small dataset and need to adapt the model to new categories. This leverages transfer learning, where the pre-trained weights are used as a starting point and updated with your data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Write a custom training script that loads the pre-trained model and fine-tunes it on your dataset
Why this is correct
Fine-tuning loads the TensorFlow Hub pre-trained model's weights into a custom training script and continues training on the small labelled dataset, adapting output categories. This suits limited data far better than training from scratch, which would require far more examples.
- ✗
Deploy the pre-trained model as-is via Vertex AI JumpStart
Why it's wrong here
JumpStart deploys the pre-trained model unchanged, so it predicts the original ImageNet classes and cannot recognise your custom categories. It is tempting because JumpStart offers one-click deployment of hub models, and it would be correct when the existing label set already matches your classification task without any adaptation.
- ✗
Build a custom container with the pre-trained model and deploy to Vertex AI Endpoints
Why it's wrong here
A custom container packages the pre-trained model but performs no retraining, so it still outputs the original ImageNet classes rather than your custom categories. It is tempting because containers give deployment flexibility, and this would be correct when serving an already-trained model requiring bespoke dependencies, not adapting one to new labels.
- ✗
Use Vertex AI AutoML for image classification
Why it's wrong here
AutoML trains a new model from your labelled images rather than adapting the TensorFlow Hub pre-trained model, discarding its learned features. It is tempting because AutoML handles small datasets well, and it would be correct when you have no suitable pre-trained checkpoint and want Google to architect and train the model for you.
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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.