The correct choice is that the model is overfitting due to the small dataset, so you should use a pre-trained checkpoint and fine-tune only the top layers. BERT contains hundreds of millions of parameters, and fine-tuning on only 10,000 samples without proper transfer learning often leads to the model memorizing noise rather than learning generalizable patterns, which directly explains why BERT accuracy is low on 10,000 samples and how to fix it. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of the bias-variance tradeoff and transfer learning best practices—a common trap is to blame the learning rate or infrastructure, but the root cause is almost always insufficient data for full fine-tuning. Remember the memory tip: “10K samples? Freeze the layers and only train the head.”
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 is fine-tuning a BERT model on Amazon SageMaker for a text classification task. The training script uses PyTorch and Hugging Face Transformers. The training job completes successfully, but the final model accuracy is low. The dataset has 10,000 labeled samples. What is the most likely cause and solution?
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
✓
The model is overfitting due to small dataset; use a pre-trained checkpoint and fine-tune only top layers
Option C is correct because BERT is large and 10,000 samples may not be enough; using a pre-trained checkpoint and doing transfer learning is standard. Option A (learning rate) is possible but not most likely. Option B (SageMaker error) is unlikely. Option D (instance type) doesn't affect accuracy directly.
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.
✗
The instance type is insufficient; use a larger instance
Why it's wrong here
Instance size affects speed, not accuracy.
✓
The model is overfitting due to small dataset; use a pre-trained checkpoint and fine-tune only top layers
Why this is correct
Fine-tuning a pre-trained BERT on a small dataset may overfit; using a pre-trained checkpoint and freezing lower layers helps.
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.
✗
The learning rate is too high; reduce it
Why it's wrong here
High learning rate can cause instability, but low accuracy suggests underfitting due to small data.
✗
The training script has a bug in the data loader
Why it's wrong here
If training completed, data loader likely works.
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: The model is overfitting due to small dataset; use a pre-trained checkpoint and fine-tune only top layers — Option C is correct because BERT is large and 10,000 samples may not be enough; using a pre-trained checkpoint and doing transfer learning is standard. Option A (learning rate) is possible but not most likely. Option B (SageMaker error) is unlikely. Option D (instance type) doesn't affect accuracy directly.
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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