- A
One-hot encoding
Why wrong: One-hot encoding transforms categories into binary vectors but does not handle missing values.
- B
Mean imputation
Why wrong: Mean imputation is appropriate for numerical features, not categorical.
- C
Mode imputation
Mode imputation replaces missing categorical values with the most frequent category, a common practice.
- D
Standard scaling
Why wrong: Standard scaling normalizes numerical features, not for missing values.
Question 910 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Which technique is commonly used to handle…
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.
Which technique is commonly used to handle missing values in a categorical feature?
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
✓
Mode imputation
Mode imputation is the standard technique for handling missing values in categorical features because it replaces missing entries with the most frequent category, preserving the feature's distribution without introducing artificial values. Unlike numerical imputation methods, mode imputation respects the non-numeric nature of categorical data and maintains the integrity of the original categories.
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.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding transforms categories into binary vectors but does not handle missing values.
- ✗
Mean imputation
Why it's wrong here
Mean imputation is appropriate for numerical features, not categorical.
- ✓
Mode imputation
Why this is correct
Mode imputation replaces missing categorical values with the most frequent category, a common practice.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Standard scaling
Why it's wrong here
Standard scaling normalizes numerical features, not for missing values.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The MLA-C01 exam often tests the distinction between data preprocessing techniques (imputation) and feature engineering techniques (encoding, scaling), leading candidates to mistakenly select one-hot encoding as a missing-value handler because it is commonly associated with categorical data.
Detailed technical explanation
How to think about this question
Under the hood, mode imputation calculates the mode (most frequent value) from the non-missing entries of the categorical feature, often using a hash map or frequency count, and then fills NaN values with that mode. A subtle behavior is that if multiple categories tie for the highest frequency, implementations may default to the first encountered mode or raise an error, so practitioners should explicitly handle ties (e.g., by using a random choice or a domain-specific default). In real-world scenarios like customer segmentation, mode imputation is preferred over dropping rows because it retains sample size while avoiding the introduction of artificial categories that could bias downstream models.
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: Mode imputation — Mode imputation is the standard technique for handling missing values in categorical features because it replaces missing entries with the most frequent category, preserving the feature's distribution without introducing artificial values. Unlike numerical imputation methods, mode imputation respects the non-numeric nature of categorical data and maintains the integrity of the original categories.
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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