- A
Load ResNet50, set trainable=False for all layers, and replace the final dense layer only
Why wrong: This does not add enough capacity; typically you add a few new layers for adaptation.
- B
Load ResNet50, freeze all layers, add new classification layers, and train only the new layers
This is the standard fine-tuning approach for resource-constrained deployment.
- C
Load ResNet50 from Keras Applications, set trainable=True for all layers, add new layers, and train the entire model
Why wrong: This would update all layers, potentially causing overfitting and losing pre-trained features.
- D
Use AutoML Vision to transfer learn without coding
Why wrong: AutoML Vision does not allow layer-level freezing control.
Question 292 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 team is training a large image classification model using transfer learning from a pre-trained ResNet50. The model will be deployed on mobile devices. They want to fine-tune only the last few layers while keeping the earlier layers frozen. Which approach should they use?
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
✓
Load ResNet50, freeze all layers, add new classification layers, and train only the new layers
Transfer learning typically involves loading a pre-trained model (e.g., ResNet50 from Keras Applications) without the top classification layer, freezing all layers, adding new trainable layers on top, and then training. The base model's layers are frozen (trainable=False). After initial training, one can optionally unfreeze some top layers.
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.
- ✗
Load ResNet50, set trainable=False for all layers, and replace the final dense layer only
Why it's wrong here
This does not add enough capacity; typically you add a few new layers for adaptation.
- ✓
Load ResNet50, freeze all layers, add new classification layers, and train only the new layers
Why this is correct
This is the standard fine-tuning approach for resource-constrained deployment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Load ResNet50 from Keras Applications, set trainable=True for all layers, add new layers, and train the entire model
Why it's wrong here
This would update all layers, potentially causing overfitting and losing pre-trained features.
- ✗
Use AutoML Vision to transfer learn without coding
Why it's wrong here
AutoML Vision does not allow layer-level freezing control.
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: Load ResNet50, freeze all layers, add new classification layers, and train only the new layers — Transfer learning typically involves loading a pre-trained model (e.g., ResNet50 from Keras Applications) without the top classification layer, freezing all layers, adding new trainable layers on top, and then training. The base model's layers are frozen (trainable=False). After initial training, one can optionally unfreeze some top layers.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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