- A
Use a different algorithm.
Why wrong: The built-in algorithm is appropriate; underfitting is not algorithm-specific.
- B
Add more layers to the model architecture.
Why wrong: Adding layers increases capacity but may cause overfitting with limited data.
- C
Use a smaller batch size.
Why wrong: Smaller batch size can improve convergence but may not resolve underfitting.
- D
Increase the number of training epochs.
Correct: More epochs allow the model to learn patterns better, reducing underfitting.
Question 243 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Using SageMaker to train a model for image…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 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
✓
Increase the number of training epochs.
Underfitting occurs when the model has not learned enough from the training data, often because training was stopped too early. Increasing the number of training epochs allows the model more iterations to converge to a better solution, which directly addresses underfitting by giving the optimizer more time to minimize the loss function.
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
The trap here is that candidates confuse underfitting with overfitting and choose to reduce batch size or change the algorithm, when the correct diagnostic for underfitting is to increase training time or model capacity, not to reduce data exposure.
Detailed technical explanation
How to think about this question
Underfitting is characterized by high bias, where the model fails to capture the underlying patterns in the data. In transfer learning, the pre-trained weights are often frozen or fine-tuned; increasing epochs allows the optimizer (e.g., SGD with momentum) to adjust the final classification layers more thoroughly. A typical symptom is that both training and validation accuracy are low and plateauing, indicating the model needs more gradient updates to fit the training distribution.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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. — Underfitting occurs when the model has not learned enough from the training data, often because training was stopped too early. Increasing the number of training epochs allows the model more iterations to converge to a better solution, which directly addresses underfitting by giving the optimizer more time to minimize the loss function.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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