The answer is to apply a log transformation to the target variable. This is the most appropriate approach because a right-skewed target, such as insurance claim amounts with many small values and a few extreme outliers, violates the normality assumption of many regression models. By applying a log transformation, the distribution becomes more symmetric, compressing the scale of large values and reducing the influence of outliers, which allows the model to learn patterns from the bulk of the data without being distorted by extreme claims. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing for regression tasks, often appearing in scenario-based questions where you must choose between loss functions and transformations. A common trap is selecting mean squared error, which is highly sensitive to outliers, or Poisson loss, which is designed for count data. Remember the memory tip: “Log the long tail” — when your target is right-skewed, a log transformation flattens the fat tail and tames the outliers.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Using a log transformation or modeling with a log-link function can reduce skewness and impact of outliers. Option A (Mean squared error) is sensitive to outliers. Option B (Quantile loss) is robust but less common for mean prediction. Option D (Poisson loss) is for count data. Option C (Log transformation of target) is standard for skewed continuous targets.
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 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.
Clue confirmation
The clue word "minimum / minimize" 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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply a log transformation to the target variable — Using a log transformation or modeling with a log-link function can reduce skewness and impact of outliers. Option A (Mean squared error) is sensitive to outliers. Option B (Quantile loss) is robust but less common for mean prediction. Option D (Poisson loss) is for count data. Option C (Log transformation of target) is standard for skewed continuous targets.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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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