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 techniques to handle missing data in a dataset?
Normalizing the data
Why wrong: Normalization does not handle missing values.
Adding a constant value of 0
Why wrong: Adding 0 can bias the model.
Mean imputation
Replacing missing values with the mean is a standard technique.
Synthetic Minority Over-sampling (SMOTE)
Why wrong: SMOTE is for class imbalance.
Deleting rows with missing values
Deleting rows is valid if missingness is random.
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 imputation
Mean imputation (Option C) is a valid technique for handling missing data because it replaces missing values with the mean of the observed values for that feature, preserving the overall mean of the dataset. This approach is simple and effective for numerical data that is missing completely at random (MCAR), as it does not introduce bias in the mean estimate.
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.
Normalizing the data
Why it's wrong here
Normalization does not handle missing values.
Adding a constant value of 0
Why it's wrong here
Adding 0 can bias the model.
Mean imputation
Why this is correct
Replacing missing values with the mean is a standard technique.
Related concept
Read the scenario before looking for a memorised answer.
Synthetic Minority Over-sampling (SMOTE)
Why it's wrong here
SMOTE is for class imbalance.
Deleting rows with missing values
Why this is correct
Deleting rows is valid if missingness is random.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the distinction between data preprocessing techniques (like imputation) and other unrelated techniques (like normalization or SMOTE), so the trap here is that candidates may confuse SMOTE or normalization as valid missing data handling methods because they are common preprocessing steps, but they serve entirely different purposes.
Detailed technical explanation
Mean imputation works under the assumption that data is missing completely at random (MCAR), meaning the missingness is independent of both observed and unobserved data. However, in practice, mean imputation reduces variance in the imputed feature and can distort correlations with other features, which is why more advanced techniques like multiple imputation (e.g., MICE) or model-based imputation are often preferred in real-world scenarios where data is not MCAR.
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
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Mean imputation — Mean imputation (Option C) is a valid technique for handling missing data because it replaces missing values with the mean of the observed values for that feature, preserving the overall mean of the dataset. This approach is simple and effective for numerical data that is missing completely at random (MCAR), as it does not introduce bias in the mean estimate.
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.
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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.
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