Quick Answer
The answer is deletion and imputation, specifically listwise deletion and imputing missing values with the mean. These two actions are valid because they represent the fundamental dichotomy in handling missing data techniques for machine learning: either remove the incomplete records entirely or estimate the missing values using statistical measures. Deletion works well when missingness is random and the dataset is large enough to avoid bias, while mean imputation preserves sample size by replacing gaps with the column average, though it can reduce variance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of data preprocessing trade-offs—a common trap is assuming all imputation methods are equally valid, when in fact median or mode may be preferred for skewed data. Remember the memory tip: “Delete if you have plenty, impute if you’re wary—but never leave a blank in your data dictionary.”
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 actions are valid ways to handle missing data in a dataset before training a machine learning model? (Select TWO.)
Delete rows with missing values
Row deletion is valid if missingness is random.
Remove all features that have any missing values
Why wrong: Too aggressive; may lose useful features.
Replace missing values with the maximum value
Why wrong: Not standard practice.
Ignore missing values and train the model
Why wrong: Most algorithms cannot handle missing values.
Impute missing values with the mean
Mean imputation is common.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Delete rows with missing values
Option A is correct because deleting rows with missing values (listwise deletion) is a straightforward and valid approach when the missing data is random and the dataset is large enough that the loss of rows does not significantly reduce statistical power or introduce bias. This method avoids the need to estimate missing values and is commonly used in practice when the proportion of missing data is low.
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.
Delete rows with missing values
Why this is correct
Row deletion is valid if missingness is random.
Related concept
Read the scenario before looking for a memorised answer.
Remove all features that have any missing values
Why it's wrong here
Too aggressive; may lose useful features.
Replace missing values with the maximum value
Why it's wrong here
Not standard practice.
Ignore missing values and train the model
Why it's wrong here
Most algorithms cannot handle missing values.
Impute missing values with the mean
Why this is correct
Mean imputation is common.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the misconception that 'ignoring missing values' is acceptable because some algorithms like tree-based models can technically handle missing values internally, but the exam expects explicit data preprocessing steps as part of the modeling pipeline.
Detailed technical explanation
Imputation with the mean (Option E) is a valid technique that preserves the sample size and maintains the overall mean of the feature, but it reduces variance and can weaken correlations with other variables. Under the hood, mean imputation assumes the data are missing completely at random (MCAR) and can be implemented using SimpleImputer in scikit-learn with strategy='mean'. In real-world scenarios, more sophisticated methods like KNN imputation or MICE are preferred when missingness is not random, but mean imputation remains a quick baseline approach.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Delete rows with missing values — Option A is correct because deleting rows with missing values (listwise deletion) is a straightforward and valid approach when the missing data is random and the dataset is large enough that the loss of rows does not significantly reduce statistical power or introduce bias. This method avoids the need to estimate missing values and is commonly used in practice when the proportion of missing data is low.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 24, 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.