- A
Freeze the first few layers and train the rest.
Freezing earlier layers preserves general features; training only later layers adapts to the new task.
- B
Add more convolutional layers to the model.
Why wrong: Adding more layers increases capacity, worsening overfitting.
- C
Use a larger learning rate to speed up training.
Why wrong: High learning rate can cause divergence, not prevent overfitting.
- D
Train the entire model from scratch on the custom dataset.
Why wrong: Training from scratch on a small dataset will likely overfit.
Question 459 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.
A data scientist wants to use a pre-trained ResNet model from Keras Applications and fine-tune it on a small custom dataset. Which approach should they take to avoid overfitting?
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 the first few layers and train the rest.
Freezing the earlier layers (which capture general features) and only training the later layers is a common transfer learning approach for small datasets, reducing overfitting.
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.
- ✓
Freeze the first few layers and train the rest.
Why this is correct
Freezing earlier layers preserves general features; training only later layers adapts to the new task.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add more convolutional layers to the model.
Why it's wrong here
Adding more layers increases capacity, worsening overfitting.
- ✗
Use a larger learning rate to speed up training.
Why it's wrong here
High learning rate can cause divergence, not prevent overfitting.
- ✗
Train the entire model from scratch on the custom dataset.
Why it's wrong here
Training from scratch on a small dataset will likely overfit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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 the first few layers and train the rest. — Freezing the earlier layers (which capture general features) and only training the later layers is a common transfer learning approach for small datasets, reducing overfitting.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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