Quick Answer
The answer is removing rows or columns with missing values and imputation, which fills in missing data using statistical methods like mean, median, or mode. These two techniques are fundamental for handling missing values before training because most machine learning algorithms cannot process datasets with null entries; removing problematic rows or columns eliminates incomplete records, while imputation preserves data size by estimating missing values from the available distribution. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing best practices, often appearing as a straightforward multiple-choice question where distractors include scaling, one-hot encoding, or PCA—techniques that address normalization, categorical encoding, and dimensionality reduction, not missing data. A common trap is confusing imputation with feature engineering, so remember that missing value handling is purely about data completeness, not transformation. Memory tip: “Drop or fill, not scale or drill”—if it doesn’t fix a null, it’s the wrong tool.
This MLS-C01 practice question tests your understanding of modeling. 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 TWO techniques are used to handle missing values in a dataset before training? (Choose 2.)
Mean or median imputation.
Imputation replaces missing values with central tendency.
Min-max scaling.
Why wrong: Scaling does not handle missing values.
Removing rows or columns with missing values.
Deletion is a simple approach to handle missing data.
One-hot encoding.
Why wrong: Encoding is for categorical data, not missing values.
Principal component analysis (PCA).
Why wrong: PCA is for dimensionality reduction, not missing values.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Mean or median imputation.
Option B is correct because imputation fills missing values. Option D is correct because removing rows/columns with missing data is a valid approach. Option A is wrong because scaling is for numerical features, not missing values. Option C is wrong because one-hot encoding is for categorical variables. Option E is wrong because PCA is for dimensionality reduction.
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.
Mean or median imputation.
Why this is correct
Imputation replaces missing values with central tendency.
Related concept
Read the scenario before looking for a memorised answer.
Min-max scaling.
Why it's wrong here
Scaling does not handle missing values.
Removing rows or columns with missing values.
Why this is correct
Deletion is a simple approach to handle missing data.
Related concept
Read the scenario before looking for a memorised answer.
One-hot encoding.
Why it's wrong here
Encoding is for categorical data, not missing values.
Principal component analysis (PCA).
Why it's wrong here
PCA is for dimensionality reduction, not missing values.
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 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
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Mean or median imputation. — Option B is correct because imputation fills missing values. Option D is correct because removing rows/columns with missing data is a valid approach. Option A is wrong because scaling is for numerical features, not missing values. Option C is wrong because one-hot encoding is for categorical variables. Option E is wrong because PCA is for dimensionality reduction.
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.
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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