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 model has high training accuracy but low validation accuracy. Which action is least likely to reduce overfitting?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Use dropout
Why wrong: Dropout randomly drops units during training, acting as regularization.
Increase regularization strength
Why wrong: Regularization penalizes large weights, reducing overfitting.
Add more training data
Why wrong: More data can help the model generalize better.
Increase model complexity
Increasing complexity makes the model more prone to overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Increase model complexity
Increasing model complexity (e.g., adding more layers or parameters) makes the model more flexible, which typically exacerbates overfitting by allowing it to memorize noise in the training data. Since the goal is to reduce overfitting, this action is counterproductive and therefore the least likely to help.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Use dropout
Why it's wrong here
Dropout randomly drops units during training, acting as regularization.
Increase regularization strength
Why it's wrong here
Regularization penalizes large weights, reducing overfitting.
Add more training data
Why it's wrong here
More data can help the model generalize better.
Increase model complexity
Why this is correct
Increasing complexity makes the model more prone to overfitting.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the misconception that 'more complex models always perform better,' leading candidates to incorrectly select increasing model complexity as a solution to overfitting rather than recognizing it as a cause.
Detailed technical explanation
Overfitting occurs when a model learns the training data's noise rather than the underlying distribution, often indicated by a large gap between training and validation accuracy. Techniques like dropout (Srivastava et al., 2014) and L2 regularization (weight decay) explicitly constrain model capacity, while increasing complexity (e.g., deeper networks or more units) expands the hypothesis space, making overfitting more likely unless accompanied by strong regularization or more data. In practice, a model with high training accuracy but low validation accuracy is a classic sign of overfitting, and adding complexity would widen this gap.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
The correct answer is: Increase model complexity — Increasing model complexity (e.g., adding more layers or parameters) makes the model more flexible, which typically exacerbates overfitting by allowing it to memorize noise in the training data. Since the goal is to reduce overfitting, this action is counterproductive and therefore the least likely to help.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 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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