- A
Label encoding
Why wrong: Label encoding imposes an arbitrary ordinal relationship that may mislead the tree model, as it treats the encoded values as continuous.
- B
Target encoding with smoothing
Target encoding replaces each category with the mean target value, optionally with smoothing to avoid overfitting. This is a standard technique for high-cardinality features in gradient boosting.
- C
One-hot encoding
Why wrong: One-hot encoding would create over 5,000 binary columns, leading to high dimensionality and sparse data, which is inefficient for gradient boosting.
- D
Drop the feature
Why wrong: Dropping the feature loses potentially valuable information without justification.
Question 7 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning engineer is using Amazon…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 machine learning engineer is using Amazon SageMaker Data Wrangler to prepare a dataset with a categorical feature that has over 5,000 distinct values (high cardinality). The engineer needs to transform this feature into a form suitable for a gradient boosting model while preserving as much information as possible. Which transform should be applied?
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
✓
Target encoding with smoothing
For high-cardinality categorical features in tree-based models like gradient boosting, target encoding (mean target per category) is effective because it captures the relationship with the target without exploding feature dimensions. One-hot encoding would create too many columns, and label encoding introduces ordinality issues.
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.
- ✗
Label encoding
Why it's wrong here
Label encoding imposes an arbitrary ordinal relationship that may mislead the tree model, as it treats the encoded values as continuous.
- ✓
Target encoding with smoothing
Why this is correct
Target encoding replaces each category with the mean target value, optionally with smoothing to avoid overfitting. This is a standard technique for high-cardinality features in gradient boosting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding would create over 5,000 binary columns, leading to high dimensionality and sparse data, which is inefficient for gradient boosting.
- ✗
Drop the feature
Why it's wrong here
Dropping the feature loses potentially valuable information without justification.
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 MLA-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
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Target encoding with smoothing — For high-cardinality categorical features in tree-based models like gradient boosting, target encoding (mean target per category) is effective because it captures the relationship with the target without exploding feature dimensions. One-hot encoding would create too many columns, and label encoding introduces ordinality issues.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-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.
About these practice questions
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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