Quick Answer
The answer is target encoding using the mean house price per zip code. This technique, also known as mean encoding, is the best approach for encoding high-cardinality categorical features because it directly captures the relationship between each category and the target variable—in this case, the average house price per zip code—without exploding the feature space. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering trade-offs: one-hot encoding would create 1,000 dummy variables, causing dimensionality and sparsity issues, while label encoding wrongly implies ordinality, and frequency encoding may lose predictive signal. A common trap is assuming label encoding is safe for tree-based models, but target encoding is preferred here for linear regression. Remember the memory tip: “High cardinality? Target the mean—don’t one-hot the lot.”
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 data scientist is building a regression model to predict house prices. The dataset includes a feature 'zip_code' with 1,000 unique values. What is the best way to handle this categorical feature in the exploratory data analysis phase?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
One-hot encode the zip_code feature
Why wrong: Why B is wrong
Apply target encoding using the mean house price per zip code
Why A is correct
Replace zip_code with the frequency of each zip code in the dataset
Why wrong: Why D is wrong
Use label encoding: assign each zip code a unique integer
Why wrong: Why C is wrong
Answer choices
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 using the mean house price per zip code
Option A is correct because target encoding (mean encoding) captures the relationship between the category and the target, and is suitable for high-cardinality features. Option B is wrong because one-hot encoding would create too many dummy variables. Option C is wrong because label encoding implies ordinality which is not present. Option D is wrong because frequency encoding may not capture price variation well.
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.
One-hot encode the zip_code feature
Why it's wrong here
Why B is wrong
Apply target encoding using the mean house price per zip code
Why this is correct
Why A is correct
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Replace zip_code with the frequency of each zip code in the dataset
Why it's wrong here
Why D is wrong
Use label encoding: assign each zip code a unique integer
Why it's wrong here
Why C is wrong
Common exam traps
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
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.
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
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.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Apply target encoding using the mean house price per zip code — Option A is correct because target encoding (mean encoding) captures the relationship between the category and the target, and is suitable for high-cardinality features. Option B is wrong because one-hot encoding would create too many dummy variables. Option C is wrong because label encoding implies ordinality which is not present. Option D is wrong because frequency encoding may not capture price variation well.
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.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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