The answer is to apply target encoding with smoothing to the zip code feature. This technique replaces each high cardinality category with the mean of the target variable for that category, then applies a smoothing factor to shrink estimates toward the global mean, effectively reducing overfitting while retaining predictive signal. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to handle categorical features with thousands of unique values—a common trap is choosing one-hot encoding, which would explode the feature space and worsen overfitting for linear models like logistic regression. Remember that target encoding with smoothing balances bias and variance, making it ideal for high cardinality features where label encoding would falsely imply ordinal relationships. A useful memory tip: think “shrinkage for stability”—smoothing pulls extreme category means toward the overall average, preventing the model from memorizing noise.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 binary classification model to predict whether a customer will subscribe to a service. The dataset contains 20 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The scientist uses a logistic regression model and obtains a training AUC of 0.85 and a test AUC of 0.60. The scientist suspects overfitting due to high cardinality features. Which approach should the scientist use to address this issue?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply target encoding with smoothing to the zip code feature
Option C (target encoding with smoothing) reduces cardinality while preserving predictive power. Option A (one-hot encoding) increases dimensionality drastically. Option B (label encoding) may introduce ordinality issues. Option D (remove zip code) may lose important information.
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.
✗
Apply label encoding to the zip code feature
Why it's wrong here
Label encoding implies ordinal relationship, which is inappropriate.
✗
Remove the zip code feature entirely
Why it's wrong here
Removing may discard useful geographic information.
✓
Apply target encoding with smoothing to the zip code feature
Why this is correct
Target encoding reduces cardinality and can improve generalization.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply one-hot encoding to the zip code feature
Why it's wrong here
One-hot encoding creates thousands of sparse features, worsening overfitting.
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 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 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 target encoding with smoothing to the zip code feature — Option C (target encoding with smoothing) reduces cardinality while preserving predictive power. Option A (one-hot encoding) increases dimensionality drastically. Option B (label encoding) may introduce ordinality issues. Option D (remove zip code) may lose important information.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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