- A
Add dropout regularization.
Reduces overfitting by randomly dropping units, effective in distributed settings.
- B
Use early stopping with patience.
Why wrong: Stops training but does not improve generalization during training.
- C
Reduce the learning rate.
Why wrong: May slow convergence but not directly prevent overfitting.
- D
Increase the batch size.
Why wrong: Can affect generalization but not a standard regularizer.
Question 849 of 1,000
hardMultiple ChoiceObjective-mapped
How to Address Overfitting in Distributed Training with Dropout Regularization
This PMLE practice question tests your understanding of pmle exam topics. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 machine learning engineer is training a large-scale text classification model using a distributed strategy on TPUs. The training loss decreases normally but the validation loss starts increasing after a few epochs while training loss continues to decrease. The engineer suspects overfitting. Which technique is most appropriate to address this while scaling training?
Quick Answer
The answer is to add dropout regularization, as it is the most appropriate technique to address overfitting in distributed training while scaling with TPUs. Dropout works by randomly deactivating a fraction of neurons during each forward pass, which forces the network to learn more robust features and prevents co-adaptation—a key cause of overfitting when training loss continues to drop but validation loss rises. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of regularization strategies that remain effective under distributed data parallelism, where techniques like early stopping only mask the problem and batch size adjustments primarily affect optimization stability, not generalization directly. A common trap is confusing overfitting with learning rate issues; remember that dropout directly targets model complexity, not convergence speed. Memory tip: think of dropout as a “team-building exercise” for neurons—each neuron must learn to work without relying on any specific teammate, making the whole model more resilient.
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
✓
Add dropout regularization.
Option A is correct because dropout regularization is a widely used technique to prevent overfitting in neural networks by randomly dropping units during training, which forces the model to learn more robust features. It can be applied in distributed training on TPUs without major modifications and directly addresses overfitting by reducing co-adaptation of neurons. Option B is wrong because early stopping halts training when validation loss increases, but it does not prevent the underlying overfitting from occurring during training. Option C is wrong because reducing the learning rate controls the step size of gradient updates and is primarily used for convergence issues, not directly for overfitting. Option D is wrong because increasing batch size can sometimes improve generalization due to reduced gradient noise, but it is not a primary or most appropriate method to combat overfitting; it may even hurt generalization in some cases.
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.
- ✓
Add dropout regularization.
Why this is correct
Reduces overfitting by randomly dropping units, effective in distributed settings.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use early stopping with patience.
Why it's wrong here
Stops training but does not improve generalization during training.
- ✗
Reduce the learning rate.
Why it's wrong here
May slow convergence but not directly prevent overfitting.
- ✗
Increase the batch size.
Why it's wrong here
Can affect generalization but not a standard regularizer.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Add dropout regularization. — Option A is correct because dropout regularization is a widely used technique to prevent overfitting in neural networks by randomly dropping units during training, which forces the model to learn more robust features. It can be applied in distributed training on TPUs without major modifications and directly addresses overfitting by reducing co-adaptation of neurons. Option B is wrong because early stopping halts training when validation loss increases, but it does not prevent the underlying overfitting from occurring during training. Option C is wrong because reducing the learning rate controls the step size of gradient updates and is primarily used for convergence issues, not directly for overfitting. Option D is wrong because increasing batch size can sometimes improve generalization due to reduced gradient noise, but it is not a primary or most appropriate method to combat overfitting; it may even hurt generalization in some cases.
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: Jun 24, 2026
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