The answer is that the model is most likely suffering from the curse of dimensionality, but only if the dataset is small relative to the 100 new binary features created. One-hot encoding high cardinality features like zip_code with 100 unique values forces the model to learn from a sparse, high-dimensional space where data points become isolated, making it difficult for a regression model to generalize. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how feature engineering choices directly impact model performance, often appearing as a trap where candidates overlook the ratio of features to samples. A common memory tip is "more columns, more sparseness, more overfitting"—if your feature count approaches or exceeds your sample count, you have entered the curse of dimensionality zone.
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 team is building a regression model to predict house prices. The dataset includes a column 'zip_code' with 100 unique values. The data scientist one-hot encodes this column, resulting in 100 new binary columns. The model shows poor performance on a validation set. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
One-hot encoding introduced multicollinearity among the binary columns.
Why wrong: While one-hot encoded columns are linearly dependent, this is typically handled by dropping one level; multicollinearity is not the main cause of poor performance.
B
One-hot encoding reduced the number of features, causing underfitting.
Why wrong: One-hot encoding increases the number of features, not reduces.
C
The one-hot encoding introduced high variance, but the validation set has low variance.
Why wrong: The issue is not about variance in the target but the high-dimensional feature space.
D
The model suffers from the curse of dimensionality due to the large number of features.
With 100 additional sparse features, the model may overfit and not generalize well.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model suffers from the curse of dimensionality due to the large number of features.
One-hot encoding 'zip_code' with 100 unique values creates 100 binary features. With only 100 features, the dataset is not high-dimensional enough to cause the curse of dimensionality, which typically requires thousands of features. The poor performance is more likely due to other issues like overfitting or data leakage, not the curse of dimensionality. Option D is incorrect because the curse of dimensionality is not the most likely cause in this scenario.
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.
✗
One-hot encoding introduced multicollinearity among the binary columns.
Why it's wrong here
While one-hot encoded columns are linearly dependent, this is typically handled by dropping one level; multicollinearity is not the main cause of poor performance.
✗
One-hot encoding reduced the number of features, causing underfitting.
Why it's wrong here
One-hot encoding increases the number of features, not reduces.
✗
The one-hot encoding introduced high variance, but the validation set has low variance.
Why it's wrong here
The issue is not about variance in the target but the high-dimensional feature space.
✓
The model suffers from the curse of dimensionality due to the large number of features.
Why this is correct
With 100 additional sparse features, the model may overfit and not generalize well.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that one-hot encoding always causes multicollinearity or the curse of dimensionality, when in reality the primary risk is overfitting due to sparse representation of high-cardinality categories.
Detailed technical explanation
How to think about this question
The curse of dimensionality refers to the phenomenon where the feature space becomes so sparse that distance metrics lose meaning, and models require exponentially more data to generalize. With 100 features, this is rarely an issue unless the dataset has very few samples. In practice, one-hot encoding a high-cardinality categorical feature often leads to overfitting because each level has few samples, causing the model to memorize noise rather than learn general patterns.
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: The model suffers from the curse of dimensionality due to the large number of features. — One-hot encoding 'zip_code' with 100 unique values creates 100 binary features. With only 100 features, the dataset is not high-dimensional enough to cause the curse of dimensionality, which typically requires thousands of features. The poor performance is more likely due to other issues like overfitting or data leakage, not the curse of dimensionality. Option D is incorrect because the curse of dimensionality is not the most likely cause in this scenario.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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