- A
Polynomial Features: Creates new features as powers of existing features.
Polynomial Features generate interaction and power terms to capture nonlinear relationships.
- B
One-Hot Encoding: Converts categorical variables into binary columns.
One-Hot Encoding creates dummy variables for each category to avoid ordinal assumptions.
- C
Feature Scaling: Standardizes numerical features to have zero mean and unit variance.
Standardization ensures features contribute equally to distance-based algorithms.
- D
Polynomial Features: Converts categorical variables into numerical codes.
Why wrong: Incorrect — this describes Label Encoding, not Polynomial Features.
- E
One-Hot Encoding: Creates polynomial combinations of features.
Why wrong: Incorrect — this describes Polynomial Features or Interaction Features, not One-Hot Encoding.
Match Feature Engineering Techniques
This PMLE practice question tests your understanding of polynomial features. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: polynomial Features. 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.
Match each feature engineering technique to its description.
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
Polynomial Features: Creates new features as powers of existing features.
This matching question tests your understanding of common feature engineering techniques. Polynomial Features create new features by raising existing features to a power (e.g., x², x³). One-Hot Encoding converts categorical variables into binary columns (0/1) for each category. Feature Scaling standardizes numerical features to have zero mean and unit variance (z-score normalization). Options D and E are incorrect: D confuses Polynomial Features with Label Encoding (which assigns numerical codes), and E confuses One-Hot Encoding with Polynomial Features.
Key principle: Polynomial Features
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Polynomial Features: Creates new features as powers of existing features.
Why this is correct
Polynomial Features generate interaction and power terms to capture nonlinear relationships.
Related concept
Polynomial Features
- ✓
One-Hot Encoding: Converts categorical variables into binary columns.
Why this is correct
One-Hot Encoding creates dummy variables for each category to avoid ordinal assumptions.
Related concept
Polynomial Features
- ✓
Feature Scaling: Standardizes numerical features to have zero mean and unit variance.
Why this is correct
Standardization ensures features contribute equally to distance-based algorithms.
Related concept
Polynomial Features
- ✗
Polynomial Features: Converts categorical variables into numerical codes.
Why it's wrong here
Incorrect — this describes Label Encoding, not Polynomial Features.
- ✗
One-Hot Encoding: Creates polynomial combinations of features.
Why it's wrong here
Incorrect — this describes Polynomial Features or Interaction Features, not One-Hot Encoding.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is mixing up Label Encoding (assigning ordinal numbers) with One-Hot Encoding (binary columns), or confusing Polynomial Features with interaction terms. Remember that Polynomial Features only involve powers of existing features, not combinations of different features.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Polynomial Features
- One-Hot Encoding
- Feature Scaling
- Label Encoding
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
Polynomial Features
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. Polynomial Features 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 polynomial Features, then practise related PMLE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PMLE question test?
Polynomial Features
What is the correct answer to this question?
The correct answer is: Polynomial Features: Creates new features as powers of existing features. — This matching question tests your understanding of common feature engineering techniques. Polynomial Features create new features by raising existing features to a power (e.g., x², x³). One-Hot Encoding converts categorical variables into binary columns (0/1) for each category. Feature Scaling standardizes numerical features to have zero mean and unit variance (z-score normalization). Options D and E are incorrect: D confuses Polynomial Features with Label Encoding (which assigns numerical codes), and E confuses One-Hot Encoding with Polynomial Features.
What should I do if I get this PMLE question wrong?
Review polynomial Features, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Polynomial Features
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Last reviewed: Jun 11, 2026
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.
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