- A
Ordinal encoding
Why wrong: Ordinal encoding assigns ordered integers, not appropriate for nominal data.
- B
Label encoding
Why wrong: Label encoding assigns arbitrary integers, implying an order that may mislead the model.
- C
One-hot encoding
One-hot encoding creates dummy variables, avoiding ordinal assumptions.
- D
Target encoding
Why wrong: Target encoding uses the target mean, which can cause overfitting and is not necessary for a small number of categories.
Question 60 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist needs to encode a categorical…
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 data scientist needs to encode a categorical feature with 6 distinct values for a linear regression model. The feature has no ordinal relationship. Which encoding method should they use?
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
✓
One-hot encoding
One-hot encoding is the correct choice because it creates binary columns for each of the 6 distinct values, avoiding the implication of any ordinal relationship. This prevents the linear regression model from incorrectly interpreting numerical order as meaningful, which would introduce bias. The feature has no ordinal relationship, so one-hot encoding ensures each category is treated as an independent predictor.
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.
- ✗
Ordinal encoding
Why it's wrong here
Ordinal encoding assigns ordered integers, not appropriate for nominal data.
- ✗
Label encoding
Why it's wrong here
Label encoding assigns arbitrary integers, implying an order that may mislead the model.
- ✓
One-hot encoding
Why this is correct
One-hot encoding creates dummy variables, avoiding ordinal assumptions.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Target encoding
Why it's wrong here
Target encoding uses the target mean, which can cause overfitting and is not necessary for a small number of categories.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between label/ordinal encoding and one-hot encoding, trapping candidates who assume any integer mapping is acceptable for categorical data without considering the model's assumption of ordinality.
Detailed technical explanation
How to think about this question
One-hot encoding creates k binary dummy variables for k categories, but to avoid multicollinearity in linear regression, one category is typically dropped (the reference category). Under the hood, this transforms the categorical feature into a design matrix where each column represents a category, and the model estimates separate coefficients for each, effectively treating them as independent. In real-world scenarios, if the feature had high cardinality (e.g., 100+ categories), one-hot encoding could lead to the curse of dimensionality, but with only 6 distinct values, it is both efficient and interpretable.
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.
TExam Day Tips
- 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: One-hot encoding — One-hot encoding is the correct choice because it creates binary columns for each of the 6 distinct values, avoiding the implication of any ordinal relationship. This prevents the linear regression model from incorrectly interpreting numerical order as meaningful, which would introduce bias. The feature has no ordinal relationship, so one-hot encoding ensures each category is treated as an independent predictor.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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