- A
Apply feature scaling to normalize the data.
Why wrong: Feature scaling does not handle missing values.
- B
Remove rows or columns with missing values if they are few.
Deletion is a valid approach when missing data is minimal.
- C
Use Principal Component Analysis (PCA) to reduce dimensionality.
Why wrong: PCA is for dimensionality reduction, not missing value treatment.
- D
Apply one-hot encoding to the missing values.
Why wrong: One-hot encoding is for categorical variables, not for missing values.
- E
Impute missing values with the mean or median of the column.
Mean/median imputation is a common technique.
Question 749 of 1,755
Handling Missing Values in Exploratory Data Analysis
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. A key principle to apply: missing value imputation. 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 TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)
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
✓
Remove rows or columns with missing values if they are few.
The correct techniques for handling missing values are removing rows/columns with missing values (if the proportion is small) and imputing missing values with statistical measures like the mean or median. Options A (feature scaling), C (PCA), and D (one-hot encoding) are not methods for dealing with missing data; they serve other purposes such as normalization, dimensionality reduction, and encoding categorical variables.
Key principle: Missing value imputation
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply feature scaling to normalize the data.
Why it's wrong here
Feature scaling does not handle missing values.
- ✓
Remove rows or columns with missing values if they are few.
Why this is correct
Deletion is a valid approach when missing data is minimal.
Related concept
Missing value imputation
- ✗
Use Principal Component Analysis (PCA) to reduce dimensionality.
Why it's wrong here
PCA is for dimensionality reduction, not missing value treatment.
- ✗
Apply one-hot encoding to the missing values.
Why it's wrong here
One-hot encoding is for categorical variables, not for missing values.
- ✓
Impute missing values with the mean or median of the column.
Why this is correct
Mean/median imputation is a common technique.
Related concept
Missing value imputation
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Missing value imputation
- Listwise deletion
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
Missing value imputation
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. Missing value imputation 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.
Review missing value imputation, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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Exploratory Data Analysis — study guide chapter
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Exploratory Data Analysis practice questions
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Missing value imputation.
What is the correct answer to this question?
The correct answer is: Remove rows or columns with missing values if they are few. — The correct techniques for handling missing values are removing rows/columns with missing values (if the proportion is small) and imputing missing values with statistical measures like the mean or median. Options A (feature scaling), C (PCA), and D (one-hot encoding) are not methods for dealing with missing data; they serve other purposes such as normalization, dimensionality reduction, and encoding categorical variables.
What should I do if I get this MLS-C01 question wrong?
Review missing value imputation, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Missing value imputation
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Last reviewed: Jun 20, 2026
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