- A
Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.
Why wrong: PCA reduces interpretability and does not handle categorical features natively.
- B
Use mutual information to select top features and apply label encoding to categorical variables.
Why wrong: Mutual information is useful but does not address high cardinality; label encoding implies ordinality.
- C
Use chi-squared test to select top features and one-hot encode categorical variables.
Why wrong: Chi-squared is for categorical targets; one-hot encoding with high cardinality creates too many features.
- D
Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.
Correlation filter reduces redundancy; target encoding converts categoricals to numeric without increasing dimensionality.
Quick Answer
The answer is to apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables. This combination is correct because correlation-based selection eliminates redundant numeric features without distorting their original meaning, while target encoding converts high-cardinality categoricals into a single numeric column based on the target mean, preserving interpretability and avoiding dimension explosion. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of dimensionality reduction preserving interpretability with high cardinality categoricals, often appearing as a trap where candidates mistakenly choose PCA or one-hot encoding. PCA sacrifices interpretability by creating uninterpretable components, and one-hot encoding with high cardinality would create hundreds of sparse columns, harming both model performance and exploratory data analysis. A useful memory tip: think “correlation cuts redundancy, target encoding tames cardinality” to recall that interpretable reduction requires removing what’s redundant and compressing what’s categorical.
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.
A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?
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
Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.
Option A is correct because correlation-based feature selection removes redundant features, and target encoding handles high-cardinality categoricals without expanding dimensions. Option B is wrong because PCA reduces interpretability and does not handle categoricals. Option C is wrong because chi-squared test is for categorical targets, but dataset has binary target; also one-hot encoding explodes dimensions. Option D is wrong because mutual information is used for feature selection but does not address high cardinality directly.
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.
- ✗
Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.
Why it's wrong here
PCA reduces interpretability and does not handle categorical features natively.
- ✗
Use mutual information to select top features and apply label encoding to categorical variables.
Why it's wrong here
Mutual information is useful but does not address high cardinality; label encoding implies ordinality.
- ✗
Use chi-squared test to select top features and one-hot encode categorical variables.
Why it's wrong here
Chi-squared is for categorical targets; one-hot encoding with high cardinality creates too many features.
- ✓
Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.
Why this is correct
Correlation filter reduces redundancy; target encoding converts categoricals to numeric without increasing dimensionality.
Related concept
Static NAT maps one inside address to one outside address.
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 MLS-C01 NAT questions on configuration and troubleshooting.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables. — Option A is correct because correlation-based feature selection removes redundant features, and target encoding handles high-cardinality categoricals without expanding dimensions. Option B is wrong because PCA reduces interpretability and does not handle categoricals. Option C is wrong because chi-squared test is for categorical targets, but dataset has binary target; also one-hot encoding explodes dimensions. Option D is wrong because mutual information is used for feature selection but does not address high cardinality directly.
What should I do if I get this MLS-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 MLS-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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Last reviewed: Jun 20, 2026
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