MLS-C01 Modeling Practice Question
A data scientist is building a model to predict insurance claim amounts. The target variable is right-skewed with many small claims and a few very large claims. The scientist wants to minimize the impact of outliers. Which loss function or transformation is MOST appropriate?
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
✓
Apply a log transformation to the target variable
Applying a log transformation to the target variable reduces skewness and mitigates the impact of outliers by compressing the scale of large values. This makes the distribution more symmetric and suitable for models like linear regression. Option A (mean squared error) is sensitive to outliers. Option B (quantile loss) predicts the median, which is robust but not typical for mean prediction. Option C (Poisson loss) is designed for count data, not continuous skewed targets. Option D (log transformation) is the standard approach for right-skewed continuous targets.
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 mean squared error loss without any transformation
Why it's wrong here
MSE is sensitive to outliers and will be dominated by large claims.
- ✗
Use quantile loss to predict the median
Why it's wrong here
Quantile loss predicts a quantile, not the mean, and may not be appropriate for expected claim amount.
- ✗
Use Poisson loss assuming the target follows a Poisson distribution
Why it's wrong here
Poisson loss is for non-negative count data, not continuous amounts.
- ✓
Apply a log transformation to the target variable
Why this is correct
Log transformation reduces skewness and makes the distribution more symmetric, reducing outlier impact.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.