The answer is to increase the number of training epochs. Underfitting in image classification occurs when the model has not learned the underlying patterns in the training data, often because training was stopped too early, leaving the model’s weights poorly optimized. By increasing the epochs, you give the gradient descent process more iterations to minimize the loss function, allowing the transfer-learned features to better adapt to your specific dataset. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish underfitting from overfitting and to recall that built-in SageMaker algorithms with transfer learning still require sufficient training time to fine-tune. A common trap is to assume adding layers or changing the algorithm will fix low accuracy, but underfitting is fundamentally a training duration issue, not a capacity issue. Memory tip: “Underfit? Extend the fit”—more epochs directly address insufficient learning.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 using SageMaker to train a model for image classification. They have a dataset of 10,000 images. They use SageMaker's built-in image classification algorithm with transfer learning. During training, they notice that the training job completes successfully but the model accuracy on the validation set is very low (~30%). They suspect the model is underfitting. Which action is most likely to improve accuracy?
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 number of training epochs.
Option D is correct because underfitting often results from insufficient training. Increasing the number of epochs gives the model more opportunity to learn. Option A is wrong because a smaller batch size may help but not as directly as more epochs. Option B is wrong because adding layers could lead to overfitting if not regularized. Option C is wrong because changing the algorithm may not address underfitting.
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.
✗
Use a different algorithm.
Why it's wrong here
The built-in algorithm is appropriate; underfitting is not algorithm-specific.
✗
Add more layers to the model architecture.
Why it's wrong here
Adding layers increases capacity but may cause overfitting with limited data.
✗
Use a smaller batch size.
Why it's wrong here
Smaller batch size can improve convergence but may not resolve underfitting.
✓
Increase the number of training epochs.
Why this is correct
Correct: More epochs allow the model to learn patterns better, reducing underfitting.
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 MLA-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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of training epochs. — Option D is correct because underfitting often results from insufficient training. Increasing the number of epochs gives the model more opportunity to learn. Option A is wrong because a smaller batch size may help but not as directly as more epochs. Option B is wrong because adding layers could lead to overfitting if not regularized. Option C is wrong because changing the algorithm may not address underfitting.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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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Question Discussion
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