The correct answer is imputing missing values with the feature mean and using a model that supports missing values natively, such as XGBoost. Mean imputation preserves the overall distribution of the feature without discarding data, making it a safe baseline for regression models where the central tendency is meaningful. Native support in tree-based models like XGBoost or LightGBM allows them to learn optimal split directions for missing values during training, avoiding the bias introduced by constant imputation. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between simple, effective imputation and model-native handling, while avoiding traps like dropping rows (which reduces sample size) or one-hot encoding (which is for categorical data). A key memory tip: for regression, think “mean or tree” — mean imputation for quick fixes, tree models for automatic handling.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 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
Options B and E are correct. Imputing missing values with the feature mean (B) is a common and straightforward technique. Using a model that supports missing values natively (E), like XGBoost or LightGBM, can handle missing data without explicit imputation. Option A (removing rows) may discard valuable data. Option C (constant imputation) can introduce bias. Option D (one-hot encoding) is for categorical features.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✓
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
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
ML Model Development — This question tests ML Model Development — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Replace missing values with the mean of the feature — Options B and E are correct. Imputing missing values with the feature mean (B) is a common and straightforward technique. Using a model that supports missing values natively (E), like XGBoost or LightGBM, can handle missing data without explicit imputation. Option A (removing rows) may discard valuable data. Option C (constant imputation) can introduce bias. Option D (one-hot encoding) is for categorical features.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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