The answer is to use iterative imputation, specifically Multiple Imputation by Chained Equations (MICE), because it models each missing value as a function of other variables in the dataset, thereby preserving the underlying relationships and reducing bias. Unlike simpler methods such as mean or mode imputation, which can distort variance and introduce systematic errors, MICE iteratively predicts missing entries using regression models, making it robust even when data is missing at random. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of bias-preserving imputation techniques versus naive approaches; a common trap is choosing mean imputation because it is fast, but the exam emphasizes that it can weaken correlations. Remember the memory tip: MICE “models each column with care,” avoiding the bias that simpler fill-ins share.
MLS-C01 Exploratory Data Analysis Practice Question
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. 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 company has a dataset with a large number of missing values in several columns. The data scientist wants to impute missing values without introducing bias. Which approach should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use iterative imputation (MICE) to model missing values
Option C is correct because MICE (Multiple Imputation by Chained Equations) is a sophisticated method that models each variable with missing values as a function of other variables, reducing bias. Option A is wrong because mean imputation can reduce variance and bias relationships. Option B is wrong because dropping rows loses data. Option D is wrong because mode imputation for categorical data may introduce bias if missingness is not random.
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.
✗
Remove rows with any missing values
Why it's wrong here
Deleting rows reduces sample size and may bias data if missingness is not random.
✓
Use iterative imputation (MICE) to model missing values
Why this is correct
MICE uses relationships among variables to impute, reducing bias.
Related concept
Read the scenario before looking for a memorised answer.
✗
Replace missing values with the mode of each column
Why it's wrong here
Mode imputation can introduce bias for categorical variables.
✗
Replace missing values with the mean of each column
Why it's wrong here
Mean imputation can distort distributions and correlations.
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
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.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use iterative imputation (MICE) to model missing values — Option C is correct because MICE (Multiple Imputation by Chained Equations) is a sophisticated method that models each variable with missing values as a function of other variables, reducing bias. Option A is wrong because mean imputation can reduce variance and bias relationships. Option B is wrong because dropping rows loses data. Option D is wrong because mode imputation for categorical data may introduce bias if missingness is not random.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are common techniques for handling missing values in a dataset during exploratory data analysis? (Select TWO.)
easy
A.Apply feature scaling to normalize the data.
✓ B.Remove rows or columns with missing values if they are few.
C.Use Principal Component Analysis (PCA) to reduce dimensionality.
D.Apply one-hot encoding to the missing values.
✓ E.Impute missing values with the mean or median of the column.
Why B: Imputation with mean/median and removing rows/columns are standard techniques. Options C (one-hot encoding), D (PCA), and E (feature scaling) are not for handling missing values.
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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
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