- A
Perform one-hot encoding on the feature
Why wrong: One-hot encoding is for categorical features, not numerical with missing values.
- B
Replace missing values with a constant, such as -999
Why wrong: Constant imputation can introduce a strong bias and affect model performance.
- C
Replace missing values with the mean of the feature
Mean imputation is simple and preserves data size, though it may reduce variance.
- D
Use a model that supports missing values natively, such as XGBoost
Models like XGBoost can handle missing values internally, avoiding imputation.
- E
Remove all rows with missing values
Why wrong: Removing rows can result in loss of valuable data, especially if missing values are sporadic.
Question 391 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data engineer is preparing a dataset for…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A data engineer is preparing a dataset for training a regression model. The dataset contains numerical features with missing values. Which two methods are appropriate for handling missing values? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Replace missing values with the mean of the feature
Option C is correct because replacing missing values with the mean of the feature is a standard imputation technique for numerical features in regression models. It preserves the overall distribution of the data and avoids introducing bias that could occur from simply dropping rows. This method is commonly implemented using libraries like scikit-learn's SimpleImputer with strategy='mean'.
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.
- ✗
Perform one-hot encoding on the feature
Why it's wrong here
One-hot encoding is for categorical features, not numerical with missing values.
- ✗
Replace missing values with a constant, such as -999
Why it's wrong here
Constant imputation can introduce a strong bias and affect model performance.
- ✓
Replace missing values with the mean of the feature
Why this is correct
Mean imputation is simple and preserves data size, though it may reduce variance.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a model that supports missing values natively, such as XGBoost
Why this is correct
Models like XGBoost can handle missing values internally, avoiding imputation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove all rows with missing values
Why it's wrong here
Removing rows can result in loss of valuable data, especially if missing values are sporadic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that dropping all rows with missing values is always safe, but the trap is that this can drastically reduce dataset size and introduce bias, making imputation or native handling preferable.
Detailed technical explanation
How to think about this question
Mean imputation assumes the data is missing completely at random (MCAR) and preserves the sample mean, but it reduces variance and can weaken correlations between features. In practice, for regression models, more sophisticated imputation like k-nearest neighbors or iterative imputation (e.g., MICE) may be preferred, but mean imputation remains a simple baseline. XGBoost natively handles missing values by learning the best direction to split on during training, which can outperform imputation in tree-based models.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Replace missing values with the mean of the feature — Option C is correct because replacing missing values with the mean of the feature is a standard imputation technique for numerical features in regression models. It preserves the overall distribution of the data and avoids introducing bias that could occur from simply dropping rows. This method is commonly implemented using libraries like scikit-learn's SimpleImputer with strategy='mean'.
What should I do if I get this MLA-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.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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