The answer is imputing missing values with the mean of the column and removing rows with missing values. These are both valid methods for handling missing values in a dataset before training a machine learning model because they address data incompleteness without introducing complex estimation bias—mean imputation preserves the column’s average, while listwise deletion removes incomplete cases entirely, assuming the missingness is random and the dataset is large enough to avoid significant power loss. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this topic tests your understanding of data preprocessing trade-offs, often appearing in scenario-based questions where you must choose between simple imputation and deletion versus more advanced techniques like KNN or regression imputation. A common trap is assuming mean imputation is always safe, but it can distort variance and relationships if missingness is not random. Memory tip: “Drop for random, mean for spread”—use deletion when missing data is sparse and random, mean imputation when you need to keep all rows but accept a slight variance reduction.
MLS-C01 Modeling Practice Question
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 valid methods for handling missing values in a dataset before training a machine learning model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove rows that contain missing values
Option A is correct because removing rows with missing values (listwise deletion) is a straightforward and valid method 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 approach ensures that only complete cases are used for training, avoiding the need to estimate missing values.
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 that contain missing values
Why this is correct
If the proportion of missing data is small, dropping rows is a valid option.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a decision tree algorithm that handles missing values internally
Why it's wrong here
This is an algorithmic approach, not a preprocessing step for handling missing values before training.
✗
Increase the number of trees in a random forest
Why it's wrong here
Increasing trees does not handle missing values; it affects model performance but not missing data.
✗
Replace missing values with zero
Why it's wrong here
Setting missing values to zero is generally not recommended unless zero has a special meaning.
✓
Impute missing values with the mean of the column
Why this is correct
Mean imputation is a simple and common method for numerical features.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that decision tree algorithms inherently handle missing values without any preprocessing, but in practice, they require explicit handling (e.g., surrogate splits) and do not automatically resolve missing data for all model training scenarios.
Detailed technical explanation
How to think about this question
Imputation with the mean (Option E) preserves the sample size and maintains the column's mean, but it reduces variance and can distort relationships between features, particularly if the missingness is not completely at random (MCAR). In practice, more sophisticated imputation methods like k-nearest neighbors or multiple imputation are often preferred to better capture the underlying data distribution.
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.
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
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove rows that contain missing values — Option A is correct because removing rows with missing values (listwise deletion) is a straightforward and valid method 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 approach ensures that only complete cases are used for training, avoiding the need to estimate missing values.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 valid techniques for handling missing values in a dataset for machine learning?
medium
A.Replace missing values with the maximum value of the feature
✓ B.Remove rows with missing values
C.Replace missing values with random noise
D.Convert missing values to the string 'missing'
✓ E.Replace missing values with the mean of the feature
Why B: Option A is correct because mean imputation is a common technique. Option C is correct because dropping rows with missing values is valid. Option B is wrong because using the maximum value introduces bias. Option D is wrong because adding random noise is not standard. Option E is wrong because converting to string is not appropriate for numerical features.
Last reviewed: Jun 24, 2026
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.
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.