hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A model has high training accuracy but low…
A model has high training accuracy but low validation accuracy. Which action is least likely to reduce overfitting?
⚠ Common exam trap
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.
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 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.
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 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.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.