Quick Answer
The answer is to impute missing values with the median of each feature. This strategy is the most appropriate because the median is robust to outliers, ensuring that the imputed values do not skew the distribution or introduce bias, while preserving the full dataset size—critical when the missing rate is low at 5% per feature. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of handling missing data during exploratory data analysis, where the common trap is choosing mean imputation (which is sensitive to outliers) or dropping rows (which would unnecessarily lose 14% of data). KNN imputation, while sophisticated, can introduce bias from neighbor selection and is computationally heavy for large datasets. Remember the memory tip: "Median for missing, mean for misleading"—when outliers are a concern, the median keeps your data clean and your model fair.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is performing EDA on a dataset with missing values in 3 of 20 features. The missing rate is 5% for each feature. The scientist wants to preserve as much data as possible while avoiding bias. Which imputation strategy is most appropriate?
Remove rows with any missing values.
Why wrong: Removing rows would discard 14% of data (assuming independent missingness).
Impute missing values with the mean of each feature.
Why wrong: Mean is sensitive to outliers.
Use K-Nearest Neighbors (KNN) imputation.
Why wrong: KNN imputation can introduce bias and is less reliable for small missing rates.
Impute missing values with the median of each feature.
Median is robust and retains data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Impute missing values with the median of each feature.
Option A is correct because median imputation is robust to outliers and preserves the dataset size. Option B is wrong because dropping rows with missing values would lose 14% of data. Option C is wrong because mean imputation can be affected by outliers. Option D is wrong because KNN imputation may introduce bias and is computationally expensive.
Key principle: OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
Answer analysis
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
Removing rows would discard 14% of data (assuming independent missingness).
Impute missing values with the mean of each feature.
Why it's wrong here
Mean is sensitive to outliers.
Use K-Nearest Neighbors (KNN) imputation.
Why it's wrong here
KNN imputation can introduce bias and is less reliable for small missing rates.
Impute missing values with the median of each feature.
Why this is correct
Median is robust and retains data.
Related concept
OSPF neighbours must agree on key parameters.
Common exam traps
OSPF neighbour formation depends on matching areas, timers, network type, authentication and passive-interface behaviour. Do not choose an answer only because the devices can ping.
Detailed technical explanation
OSPF questions usually test the details that control adjacency and route selection. Read the neighbour state, area, router ID and interface configuration before deciding what is wrong.
Key takeaway
OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough.
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. OSPF neighbour adjacency depends on matching area, hello/dead timers, network type, and authentication — IP reachability alone is not enough. 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
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — OSPF neighbours must agree on key parameters..
The correct answer is: Impute missing values with the median of each feature. — Option A is correct because median imputation is robust to outliers and preserves the dataset size. Option B is wrong because dropping rows with missing values would lose 14% of data. Option C is wrong because mean imputation can be affected by outliers. Option D is wrong because KNN imputation may introduce bias and is computationally expensive.
Review OSPF neighbour requirements — matching area type, hello and dead timers, network type, stub flags, and authentication. Study show ip ospf neighbor states (INIT, 2-WAY, FULL). Then practise related MLS-C01 OSPF questions on adjacency and route selection.
OSPF neighbours must agree on key parameters.
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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.
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