The answer is to create a binary indicator for missingness and impute the continuous values with the median. This approach is correct because it preserves the predictive signal from the feature while accounting for the pattern of missingness; the binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to handle high missing values proportion without discarding important domain knowledge—a common trap is to drop the feature entirely or use mean imputation alone, which ignores the missingness pattern. Remember the memory tip: “Flag the gap, fill with the middle”—the binary flag captures the missingness signal, and the median fills the gap robustly.
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.
During EDA, a data scientist notices that a feature has a high proportion of missing values (e.g., 70%). The feature is continuous and expected to be important based on domain knowledge. What is the best approach to handle this?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a binary indicator for missingness and impute the continuous values with the median.
Option B is correct because it preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.
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 the feature entirely to avoid bias.
Why it's wrong here
Removing a potentially important feature is not optimal.
✓
Create a binary indicator for missingness and impute the continuous values with the median.
Why this is correct
This captures both the pattern of missingness and the distribution.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Impute missing values with -1 since it is out of range.
Why it's wrong here
Arbitrary constant can distort the distribution.
✗
Drop all rows with missing values in that feature.
Why it's wrong here
Dropping 70% of rows is wasteful.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose to drop the feature or rows without considering that missingness can be a meaningful signal, and that a binary indicator combined with robust imputation is a standard technique for high-missingness continuous features.
Detailed technical explanation
How to think about this question
Under the hood, the binary missingness indicator acts as a learnable mask that allows tree-based models (e.g., XGBoost, LightGBM) to split on missingness as a separate category, while linear models can treat it as an interaction term. In production, this technique is especially useful when missingness correlates with the target (e.g., sensor failures in IoT where missing readings indicate malfunction). A subtle behavior: median imputation preserves the central tendency but reduces variance, so for highly skewed features, median is preferred over mean to avoid pulling imputed values toward outliers.
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.
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: Create a binary indicator for missingness and impute the continuous values with the median. — Option B is correct because it preserves the predictive signal from the feature while accounting for the pattern of missingness. Creating a binary indicator allows the model to learn whether missingness itself is informative, and median imputation is robust to outliers for a continuous feature. This approach avoids the bias of dropping the feature entirely and is more principled than arbitrary out-of-range imputation.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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