Quick Answer
The answer is imputation with mean or median and removing rows with missing values. Imputation with mean or median is a common technique because it preserves the dataset size by replacing null entries with a central tendency measure, avoiding bias when data is missing at random. Removing rows with missing values is equally valid, particularly when the missingness is minimal or completely at random, as it eliminates the need for estimation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of fundamental data preprocessing steps, often appearing in scenario-based questions where you must choose between imputation and deletion based on data volume and missingness pattern. A common trap is confusing handling missing values with encoding or scaling techniques—one-hot encoding is for categorical variables, PCA for dimensionality reduction, and standardization for feature scaling. Memory tip: think “fill or drop”—if the missing rate is low, drop; if moderate, fill with mean or median.
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 of the following are common techniques to handle missing values in a dataset?
Standardization
Why wrong: Standardization is for scaling features.
Principal Component Analysis (PCA)
Why wrong: PCA is for dimensionality reduction.
One-hot encoding
Why wrong: One-hot encoding is for categorical variables.
Remove rows with missing values
Removing rows is a simple approach.
Imputation with mean or median
Imputation fills missing values with a central tendency.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Remove rows with missing values
Options A and B are correct. A is correct because imputation with mean/median fills missing values. B is correct because removing rows with missing values is a valid approach. C is wrong because one-hot encoding is for categorical data, not missing values. D is wrong because PCA is for dimensionality reduction, not missing value handling. E is wrong because standardization is for scaling, not missing values.
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.
Standardization
Why it's wrong here
Standardization is for scaling features.
Principal Component Analysis (PCA)
Why it's wrong here
PCA is for dimensionality reduction.
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables.
Remove rows with missing values
Why this is correct
Removing rows is a simple approach.
Related concept
Read the scenario before looking for a memorised answer.
Imputation with mean or median
Why this is correct
Imputation fills missing values with a central tendency.
Related concept
Read the scenario before looking for a memorised answer.
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.
Modeling — study guide chapter
Learn the concepts, then practise the questions
Modeling practice questions
Targeted practice on this topic area only
All MLS-C01 questions
1,755 questions across all exam domains
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise MLS-C01 questions linked to Data Engineering.
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Practise MLS-C01 questions linked to Modeling.
Practise MLS-C01 questions linked to Exploratory Data Analysis.
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
Practise MLS-C01 questions linked to MLS-C01 scenario.
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Remove rows with missing values — Options A and B are correct. A is correct because imputation with mean/median fills missing values. B is correct because removing rows with missing values is a valid approach. C is wrong because one-hot encoding is for categorical data, not missing values. D is wrong because PCA is for dimensionality reduction, not missing value handling. E is wrong because standardization is for scaling, not missing values.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.