The correct choices are decreasing the learning rate and increasing dropout, as both directly address overfitting during BERT fine-tuning. Decreasing the learning rate prevents the model from making large, erratic weight updates that can cause it to memorize noise in the training data, while increasing dropout adds a regularization effect by randomly deactivating neurons, forcing the model to learn more robust features. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to reduce overfitting in transformer models on SageMaker, often appearing as a multi-select question where you must distinguish between regularization techniques and misguided options like adding more layers or using a larger pre-trained model, which actually increase model capacity and worsen overfitting. A common trap is assuming larger batch sizes always help, but they provide weaker regularization than dropout. Memory tip: think “LR down, drop out” to recall that lowering the learning rate and increasing dropout are your go-to pair for taming overfitting in fine-tuned BERT.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 company uses Amazon SageMaker to build a text classification model using a pre-trained BERT model. The dataset contains 10,000 labeled documents. The model is overfitting: training accuracy is 99%, validation accuracy is 85%. Which TWO of the following are most likely to help reduce overfitting? (Choose TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the dropout rate during fine-tuning
Increasing dropout during fine-tuning adds regularization. Decreasing the learning rate can help the model converge to a better solution and prevent overfitting to the training set. Increasing batch size can sometimes regularize but is not as effective as dropout. Adding more layers increases model capacity and overfitting. Using a larger pre-trained model also increases capacity.
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 more transformer layers to the model
Why it's wrong here
More layers increase model capacity and overfitting.
✓
Increase the dropout rate during fine-tuning
Why this is correct
Dropout is a regularization technique that randomly drops units, reducing overfitting.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size
Why it's wrong here
Larger batch sizes can sometimes hurt generalization and are not a standard regularization for overfitting.
✗
Use a larger pre-trained BERT model
Why it's wrong here
Larger models have more capacity and are more prone to overfitting on small datasets.
✓
Decrease the learning rate
Why this is correct
A lower learning rate can help the model generalize better by making smaller updates.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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 MLS-C01 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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the dropout rate during fine-tuning — Increasing dropout during fine-tuning adds regularization. Decreasing the learning rate can help the model converge to a better solution and prevent overfitting to the training set. Increasing batch size can sometimes regularize but is not as effective as dropout. Adding more layers increases model capacity and overfitting. Using a larger pre-trained model also increases capacity.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLS-C01 exam.
Question Discussion
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