- A
Unfreeze the last few layers and freeze the rest
Why wrong: May still overfit; more parameters than option A.
- B
Randomly reinitialise all layers and train from scratch
Why wrong: Defeats the purpose of transfer learning.
- C
Freeze all layers and train only the classifier head
Minimises overfitting by limiting trainable parameters; features are already good.
- D
Unfreeze all layers and train the entire model
Why wrong: High risk of overfitting with small dataset.
Question 406 of 1,000
PMLE Scaling Prototypes into ML Models Practice Question
This PMLE practice question tests your understanding of scaling prototypes into ml models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
You are fine-tuning a pre-trained model using transfer learning. The new dataset is small and very similar to the original training data. To avoid overfitting, which layer freezing strategy should you adopt?
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
✓
Freeze all layers and train only the classifier head
When fine-tuning a pre-trained model on a small dataset that is very similar to the original training data, the safest strategy to avoid overfitting is to freeze all layers and train only the classifier head. This preserves the rich, general-purpose feature representations learned from the original large dataset, while allowing the final classification layer to adapt to the new task. Training the entire model or unfreezing many layers on a small dataset would risk overfitting because the model would have too many parameters to update relative to the limited new samples.
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.
- ✗
Unfreeze the last few layers and freeze the rest
Why it's wrong here
May still overfit; more parameters than option A.
- ✗
Randomly reinitialise all layers and train from scratch
Why it's wrong here
Defeats the purpose of transfer learning.
- ✓
Freeze all layers and train only the classifier head
Why this is correct
Minimises overfitting by limiting trainable parameters; features are already good.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Unfreeze all layers and train the entire model
Why it's wrong here
High risk of overfitting with small dataset.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall in this exam is the misconception that unfreezing more layers always yields better fine-tuning performance. However, with a small, similar dataset, freezing all layers except the classifier head is the correct regularization strategy to prevent overfitting, not a sign of underfitting.
Detailed technical explanation
How to think about this question
Under the hood, freezing layers is implemented by setting the `requires_grad` attribute to `False` for those layers' parameters in frameworks like PyTorch or by setting `trainable=False` in TensorFlow/Keras. This prevents gradient computation and weight updates during backpropagation, effectively locking in the pre-trained feature extractors. In real-world scenarios, such as fine-tuning a ResNet-50 pre-trained on ImageNet for a medical imaging task with only a few hundred images, freezing all but the classifier head is a standard practice to leverage the hierarchical features (edges, textures, shapes) learned from millions of diverse images while avoiding catastrophic overfitting.
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.
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FAQ
Questions learners often ask
What does this PMLE question test?
Scaling Prototypes into ML Models — This question tests Scaling Prototypes into ML Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Freeze all layers and train only the classifier head — When fine-tuning a pre-trained model on a small dataset that is very similar to the original training data, the safest strategy to avoid overfitting is to freeze all layers and train only the classifier head. This preserves the rich, general-purpose feature representations learned from the original large dataset, while allowing the final classification layer to adapt to the new task. Training the entire model or unfreezing many layers on a small dataset would risk overfitting because the model would have too many parameters to update relative to the limited new samples.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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