Quick Answer
The answer is target encoding based on the mean of the target variable per category. This technique is the best approach for high cardinality categorical encoding because it compresses over one million unique values into a single numeric feature without exploding the feature space, unlike one-hot encoding which would create millions of sparse columns and degrade model performance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of feature engineering trade-offs, especially when using SageMaker Data Wrangler for large-scale data; a common trap is choosing label encoding, which introduces false ordinal relationships, or dropping the feature entirely, which discards valuable signal. Remember the memory tip: “Target mean tames the cardinality beast”—when cardinality is extreme, encode by the target’s average to preserve information while keeping dimensions manageable.
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 engineer is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The analysis reveals high cardinality in a categorical feature with over 1 million unique values. What is the best approach to handle this before training a model?
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.
Apply one-hot encoding.
Why wrong: One-hot encoding produces too many features.
Use label encoding to convert categories to integers.
Why wrong: Label encoding can imply ordinality.
Drop the high-cardinality feature.
Why wrong: Dropping may discard valuable information.
Use target encoding based on the mean of the target variable per category.
Target encoding reduces cardinality and captures target relationship.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use target encoding based on the mean of the target variable per category.
Option B is correct because target encoding is effective for high cardinality. Option A is wrong because one-hot encoding would create too many columns. Option C is wrong because label encoding may introduce ordinal relationships. Option D is wrong because dropping the feature 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
For each option: why learners choose it and why it is or isn't the right answer here.
Apply one-hot encoding.
Why it's wrong here
One-hot encoding produces too many features.
Use label encoding to convert categories to integers.
Why it's wrong here
Label encoding can imply ordinality.
Drop the high-cardinality feature.
Why it's wrong here
Dropping may discard valuable information.
Use target encoding based on the mean of the target variable per category.
Why this is correct
Target encoding reduces cardinality and captures target relationship.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
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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: Use target encoding based on the mean of the target variable per category. — Option B is correct because target encoding is effective for high cardinality. Option A is wrong because one-hot encoding would create too many columns. Option C is wrong because label encoding may introduce ordinal relationships. Option D is wrong because dropping the feature may lose important information.
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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