AI Feature Engineering MCQ Questions with Answers (Latest 2026)

Q1. Which option best describes a feature?

A. A model layer. B. A loss term. C. An input variable used by a model. D. A label or target.

Select an answer to check.

Answer: An input variable used by a model.

Here, An input variable used by a model. is the right choice. Quality features drive accuracy. It aligns directly with what the question asks about which option best describes a feature. A quick elimination of partially true options helps confirm it.

Q2. What is the primary purpose of a feature?

A. A loss term. B. A label or target. C. An input variable used by a model. D. A model layer.

Select an answer to check.

Answer: An input variable used by a model.

In this case, An input variable used by a model. is correct. Quality features drive accuracy. It aligns directly with what the question asks about what is the primary purpose of a feature. A quick elimination of partially true options helps confirm it.

Q3. Which statement about a feature is most accurate?

A. A loss term. B. A model layer. C. A label or target. D. An input variable used by a model.

Select an answer to check.

Answer: An input variable used by a model.

The best option here is An input variable used by a model.. Quality features drive accuracy. It aligns directly with what the question asks about which statement about a feature is most accurate. A quick elimination of partially true options helps confirm it.

Q4. How is a feature best characterized?

A. A label or target. B. A model layer. C. An input variable used by a model. D. A loss term.

Select an answer to check.

Answer: An input variable used by a model.

For this question, An input variable used by a model. is correct. Quality features drive accuracy. It aligns directly with what the question asks about how is a feature best characterized. A quick elimination of partially true options helps confirm it.

Q5. Which option best describes feature engineering?

A. Pure hyperparameter tuning. B. Designing/transforming features to improve models. C. Random data augmentation only. D. Adding more layers always.

Select an answer to check.

Answer: Designing/transforming features to improve models.

Designing/transforming features to improve models. is the correct answer here. Often the highest-leverage activity. It aligns directly with what the question asks about which option best describes feature engineering. A quick elimination of partially true options helps confirm it.

Q6. What is the primary purpose of feature engineering?

A. Adding more layers always. B. Designing/transforming features to improve models. C. Random data augmentation only. D. Pure hyperparameter tuning.

Select an answer to check.

Answer: Designing/transforming features to improve models.

Here, Designing/transforming features to improve models. is the right choice. Often the highest-leverage activity. This matches the core idea being tested around what is the primary purpose of feature engineering. A quick elimination of partially true options helps confirm it.

Q7. Which statement about feature engineering is most accurate?

A. Designing/transforming features to improve models. B. Adding more layers always. C. Pure hyperparameter tuning. D. Random data augmentation only.

Select an answer to check.

Answer: Designing/transforming features to improve models.

In this case, Designing/transforming features to improve models. is correct. Often the highest-leverage activity. This matches the core idea being tested around which statement about feature engineering is most accurate. A quick elimination of partially true options helps confirm it.

Q8. How is feature engineering best characterized?

A. Random data augmentation only. B. Pure hyperparameter tuning. C. Adding more layers always. D. Designing/transforming features to improve models.

Select an answer to check.

Answer: Designing/transforming features to improve models.

The best option here is Designing/transforming features to improve models.. Often the highest-leverage activity. This matches the core idea being tested around how is feature engineering best characterized. A quick elimination of partially true options helps confirm it.

Q9. Which option best describes one-hot encoding?

A. Frequency encoding. B. Mean target encoding. C. Hash encoding. D. Binary indicator vector for a categorical value.

Select an answer to check.

Answer: Binary indicator vector for a categorical value.

For this question, Binary indicator vector for a categorical value. is correct. Simple but high-cardinality issue. This matches the core idea being tested around which option best describes one-hot encoding. A quick elimination of partially true options helps confirm it.

Q10. What is the primary purpose of one-hot encoding?

A. Binary indicator vector for a categorical value. B. Hash encoding. C. Frequency encoding. D. Mean target encoding.

Select an answer to check.

Answer: Binary indicator vector for a categorical value.

Binary indicator vector for a categorical value. is the correct answer here. Simple but high-cardinality issue. This matches the core idea being tested around what is the primary purpose of one-hot encoding. A quick elimination of partially true options helps confirm it.

Q11. Which statement about one-hot encoding is most accurate?

A. Mean target encoding. B. Hash encoding. C. Frequency encoding. D. Binary indicator vector for a categorical value.

Select an answer to check.

Answer: Binary indicator vector for a categorical value.

Here, Binary indicator vector for a categorical value. is the right choice. Simple but high-cardinality issue. That is exactly the concept behind which statement about one-hot encoding is most accurate in this context. A quick elimination of partially true options helps confirm it.

Q12. How is one-hot encoding best characterized?

A. Mean target encoding. B. Hash encoding. C. Binary indicator vector for a categorical value. D. Frequency encoding.

Select an answer to check.

Answer: Binary indicator vector for a categorical value.

In this case, Binary indicator vector for a categorical value. is correct. Simple but high-cardinality issue. That is exactly the concept behind how is one-hot encoding best characterized in this context. A quick elimination of partially true options helps confirm it.

Q13. Which option best describes ordinal encoding?

A. One-hot vector. B. Bag-of-words. C. Mapping categories to integers with order. D. Random integer mapping.

Select an answer to check.

Answer: Mapping categories to integers with order.

The best option here is Mapping categories to integers with order.. Use only when categories are ordinal. That is exactly the concept behind which option best describes ordinal encoding in this context. A quick elimination of partially true options helps confirm it.

Q14. What is the primary purpose of ordinal encoding?

A. Bag-of-words. B. One-hot vector. C. Random integer mapping. D. Mapping categories to integers with order.

Select an answer to check.

Answer: Mapping categories to integers with order.

For this question, Mapping categories to integers with order. is correct. Use only when categories are ordinal. That is exactly the concept behind what is the primary purpose of ordinal encoding in this context. A quick elimination of partially true options helps confirm it.

Q15. Which statement about ordinal encoding is most accurate?

A. Mapping categories to integers with order. B. Random integer mapping. C. Bag-of-words. D. One-hot vector.

Select an answer to check.

Answer: Mapping categories to integers with order.

Mapping categories to integers with order. is the correct answer here. Use only when categories are ordinal. That is exactly the concept behind which statement about ordinal encoding is most accurate in this context. A quick elimination of partially true options helps confirm it.

Q16. How is ordinal encoding best characterized?

A. Random integer mapping. B. One-hot vector. C. Bag-of-words. D. Mapping categories to integers with order.

Select an answer to check.

Answer: Mapping categories to integers with order.

Here, Mapping categories to integers with order. is the right choice. Use only when categories are ordinal. It fits the requirement in the prompt about how is ordinal encoding best characterized. A quick elimination of partially true options helps confirm it.

Q17. Which option best describes target encoding?

A. Random IDs. B. Frequency only. C. One-hot only. D. Replace category with target stats (with smoothing).

Select an answer to check.

Answer: Replace category with target stats (with smoothing).

In this case, Replace category with target stats (with smoothing). is correct. Risk of leakage; smooth carefully. It fits the requirement in the prompt about which option best describes target encoding. A quick elimination of partially true options helps confirm it.

Q18. What is the primary purpose of target encoding?

A. Replace category with target stats (with smoothing). B. Frequency only. C. One-hot only. D. Random IDs.

Select an answer to check.

Answer: Replace category with target stats (with smoothing).

The best option here is Replace category with target stats (with smoothing).. Risk of leakage; smooth carefully. It fits the requirement in the prompt about what is the primary purpose of target encoding. A quick elimination of partially true options helps confirm it.

Q19. Which statement about target encoding is most accurate?

A. Frequency only. B. Random IDs. C. Replace category with target stats (with smoothing). D. One-hot only.

Select an answer to check.

Answer: Replace category with target stats (with smoothing).

For this question, Replace category with target stats (with smoothing). is correct. Risk of leakage; smooth carefully. It fits the requirement in the prompt about which statement about target encoding is most accurate. A quick elimination of partially true options helps confirm it.

Q20. How is target encoding best characterized?

A. One-hot only. B. Random IDs. C. Frequency only. D. Replace category with target stats (with smoothing).

Select an answer to check.

Answer: Replace category with target stats (with smoothing).

Replace category with target stats (with smoothing). is the correct answer here. Risk of leakage; smooth carefully. It fits the requirement in the prompt about how is target encoding best characterized. A quick elimination of partially true options helps confirm it.

Q21. Which option best describes frequency encoding?

A. Target encoding. B. Hashing trick. C. Replace category with its observed frequency. D. One-hot only.

Select an answer to check.

Answer: Replace category with its observed frequency.

Here, Replace category with its observed frequency. is the right choice. Cheap and works for high cardinality. This is the most accurate statement for which option best describes frequency encoding. A quick elimination of partially true options helps confirm it.

Q22. What is the primary purpose of frequency encoding?

A. Replace category with its observed frequency. B. One-hot only. C. Hashing trick. D. Target encoding.

Select an answer to check.

Answer: Replace category with its observed frequency.

In this case, Replace category with its observed frequency. is correct. Cheap and works for high cardinality. This is the most accurate statement for what is the primary purpose of frequency encoding. A quick elimination of partially true options helps confirm it.

Q23. Which statement about frequency encoding is most accurate?

A. Hashing trick. B. Target encoding. C. Replace category with its observed frequency. D. One-hot only.

Select an answer to check.

Answer: Replace category with its observed frequency.

The best option here is Replace category with its observed frequency.. Cheap and works for high cardinality. This is the most accurate statement for which statement about frequency encoding is most accurate. A quick elimination of partially true options helps confirm it.

Q24. How is frequency encoding best characterized?

A. Replace category with its observed frequency. B. Hashing trick. C. One-hot only. D. Target encoding.

Select an answer to check.

Answer: Replace category with its observed frequency.

For this question, Replace category with its observed frequency. is correct. Cheap and works for high cardinality. This is the most accurate statement for how is frequency encoding best characterized. A quick elimination of partially true options helps confirm it.

Q25. Which option best describes hashing trick?

A. Hash categories into a fixed-size space. B. Target encoding. C. Random sampling. D. Plain one-hot.

Select an answer to check.

Answer: Hash categories into a fixed-size space.

Hash categories into a fixed-size space. is the correct answer here. Bounds dim but allows collisions. This is the most accurate statement for which option best describes hashing trick. A quick elimination of partially true options helps confirm it.

Q26. What is the primary purpose of hashing trick?

A. Hash categories into a fixed-size space. B. Random sampling. C. Plain one-hot. D. Target encoding.

Select an answer to check.

Answer: Hash categories into a fixed-size space.

Here, Hash categories into a fixed-size space. is the right choice. Bounds dim but allows collisions. It aligns directly with what the question asks about what is the primary purpose of hashing trick. The other options are either incomplete or contextually incorrect.

Q27. Which statement about hashing trick is most accurate?

A. Plain one-hot. B. Target encoding. C. Random sampling. D. Hash categories into a fixed-size space.

Select an answer to check.

Answer: Hash categories into a fixed-size space.

In this case, Hash categories into a fixed-size space. is correct. Bounds dim but allows collisions. It aligns directly with what the question asks about which statement about hashing trick is most accurate. The other options are either incomplete or contextually incorrect.

Q28. How is hashing trick best characterized?

A. Random sampling. B. Target encoding. C. Hash categories into a fixed-size space. D. Plain one-hot.

Select an answer to check.

Answer: Hash categories into a fixed-size space.

The best option here is Hash categories into a fixed-size space.. Bounds dim but allows collisions. It aligns directly with what the question asks about how is hashing trick best characterized. The other options are either incomplete or contextually incorrect.

Q29. Which option best describes standardization?

A. Robust scaling. B. Min-max normalization. C. Subtract mean and divide by std (z-score). D. Quantile transform.

Select an answer to check.

Answer: Subtract mean and divide by std (z-score).

For this question, Subtract mean and divide by std (z-score). is correct. Helps gradient-based models. It aligns directly with what the question asks about which option best describes standardization. The other options are either incomplete or contextually incorrect.

Q30. What is the primary purpose of standardization?

A. Subtract mean and divide by std (z-score). B. Min-max normalization. C. Quantile transform. D. Robust scaling.

Select an answer to check.

Answer: Subtract mean and divide by std (z-score).

Subtract mean and divide by std (z-score). is the correct answer here. Helps gradient-based models. It aligns directly with what the question asks about what is the primary purpose of standardization. The other options are either incomplete or contextually incorrect.

Q31. Which statement about standardization is most accurate?

A. Min-max normalization. B. Robust scaling. C. Subtract mean and divide by std (z-score). D. Quantile transform.

Select an answer to check.

Answer: Subtract mean and divide by std (z-score).

Here, Subtract mean and divide by std (z-score). is the right choice. Helps gradient-based models. This matches the core idea being tested around which statement about standardization is most accurate. The other options are either incomplete or contextually incorrect.

Q32. How is standardization best characterized?

A. Min-max normalization. B. Robust scaling. C. Subtract mean and divide by std (z-score). D. Quantile transform.

Select an answer to check.

Answer: Subtract mean and divide by std (z-score).

In this case, Subtract mean and divide by std (z-score). is correct. Helps gradient-based models. This matches the core idea being tested around how is standardization best characterized. The other options are either incomplete or contextually incorrect.

Q33. Which option best describes min-max normalization?

A. Scale to a fixed range, e.g., [0,1]. B. Quantile transform. C. Robust scaling. D. Z-score standardization.

Select an answer to check.

Answer: Scale to a fixed range, e.g., [0,1].

The best option here is Scale to a fixed range, e.g., [0,1].. Sensitive to outliers. This matches the core idea being tested around which option best describes min-max normalization. The other options are either incomplete or contextually incorrect.

Q34. What is the primary purpose of min-max normalization?

A. Scale to a fixed range, e.g., [0,1]. B. Quantile transform. C. Z-score standardization. D. Robust scaling.

Select an answer to check.

Answer: Scale to a fixed range, e.g., [0,1].

For this question, Scale to a fixed range, e.g., [0,1]. is correct. Sensitive to outliers. This matches the core idea being tested around what is the primary purpose of min-max normalization. The other options are either incomplete or contextually incorrect.

Q35. Which statement about min-max normalization is most accurate?

A. Scale to a fixed range, e.g., [0,1]. B. Robust scaling. C. Quantile transform. D. Z-score standardization.

Select an answer to check.

Answer: Scale to a fixed range, e.g., [0,1].

Scale to a fixed range, e.g., [0,1]. is the correct answer here. Sensitive to outliers. This matches the core idea being tested around which statement about min-max normalization is most accurate. The other options are either incomplete or contextually incorrect.

Q36. How is min-max normalization best characterized?

A. Quantile transform. B. Scale to a fixed range, e.g., [0,1]. C. Robust scaling. D. Z-score standardization.

Select an answer to check.

Answer: Scale to a fixed range, e.g., [0,1].

Here, Scale to a fixed range, e.g., [0,1]. is the right choice. Sensitive to outliers. That is exactly the concept behind how is min-max normalization best characterized in this context. The other options are either incomplete or contextually incorrect.

Q37. Which option best describes robust scaling?

A. Z-score standardization. B. Min-max normalization. C. Quantile transform. D. Use median and IQR for scaling.

Select an answer to check.

Answer: Use median and IQR for scaling.

In this case, Use median and IQR for scaling. is correct. Better with outliers. That is exactly the concept behind which option best describes robust scaling in this context. The other options are either incomplete or contextually incorrect.

Q38. What is the primary purpose of robust scaling?

A. Quantile transform. B. Min-max normalization. C. Z-score standardization. D. Use median and IQR for scaling.

Select an answer to check.

Answer: Use median and IQR for scaling.

The best option here is Use median and IQR for scaling.. Better with outliers. That is exactly the concept behind what is the primary purpose of robust scaling in this context. The other options are either incomplete or contextually incorrect.

Q39. Which statement about robust scaling is most accurate?

A. Quantile transform. B. Use median and IQR for scaling. C. Min-max normalization. D. Z-score standardization.

Select an answer to check.

Answer: Use median and IQR for scaling.

For this question, Use median and IQR for scaling. is correct. Better with outliers. That is exactly the concept behind which statement about robust scaling is most accurate in this context. The other options are either incomplete or contextually incorrect.

Q40. How is robust scaling best characterized?

A. Min-max normalization. B. Z-score standardization. C. Quantile transform. D. Use median and IQR for scaling.

Select an answer to check.

Answer: Use median and IQR for scaling.

Use median and IQR for scaling. is the correct answer here. Better with outliers. That is exactly the concept behind how is robust scaling best characterized in this context. The other options are either incomplete or contextually incorrect.

Q41. Which option best describes log transform?

A. Polynomial expansion. B. Compress skewed distributions. C. One-hot encoding. D. Z-score standardization.

Select an answer to check.

Answer: Compress skewed distributions.

Here, Compress skewed distributions. is the right choice. Helps with right-skewed counts. It fits the requirement in the prompt about which option best describes log transform. The other options are either incomplete or contextually incorrect.

Q42. What is the primary purpose of log transform?

A. Compress skewed distributions. B. Z-score standardization. C. One-hot encoding. D. Polynomial expansion.

Select an answer to check.

Answer: Compress skewed distributions.

In this case, Compress skewed distributions. is correct. Helps with right-skewed counts. It fits the requirement in the prompt about what is the primary purpose of log transform. The other options are either incomplete or contextually incorrect.

Q43. Which statement about log transform is most accurate?

A. Polynomial expansion. B. One-hot encoding. C. Z-score standardization. D. Compress skewed distributions.

Select an answer to check.

Answer: Compress skewed distributions.

The best option here is Compress skewed distributions.. Helps with right-skewed counts. It fits the requirement in the prompt about which statement about log transform is most accurate. The other options are either incomplete or contextually incorrect.

Q44. How is log transform best characterized?

A. One-hot encoding. B. Polynomial expansion. C. Z-score standardization. D. Compress skewed distributions.

Select an answer to check.

Answer: Compress skewed distributions.

For this question, Compress skewed distributions. is correct. Helps with right-skewed counts. It fits the requirement in the prompt about how is log transform best characterized. The other options are either incomplete or contextually incorrect.

Q45. Which option best describes polynomial features?

A. Hashing trick. B. Pure log transform. C. Add interactions/powers of features. D. Discretization into bins.

Select an answer to check.

Answer: Add interactions/powers of features.

Add interactions/powers of features. is the correct answer here. Increases model expressiveness. It fits the requirement in the prompt about which option best describes polynomial features. The other options are either incomplete or contextually incorrect.

Q46. What is the primary purpose of polynomial features?

A. Add interactions/powers of features. B. Discretization into bins. C. Pure log transform. D. Hashing trick.

Select an answer to check.

Answer: Add interactions/powers of features.

Here, Add interactions/powers of features. is the right choice. Increases model expressiveness. This is the most accurate statement for what is the primary purpose of polynomial features. The other options are either incomplete or contextually incorrect.

Q47. Which statement about polynomial features is most accurate?

A. Pure log transform. B. Hashing trick. C. Add interactions/powers of features. D. Discretization into bins.

Select an answer to check.

Answer: Add interactions/powers of features.

In this case, Add interactions/powers of features. is correct. Increases model expressiveness. This is the most accurate statement for which statement about polynomial features is most accurate. The other options are either incomplete or contextually incorrect.

Q48. How is polynomial features best characterized?

A. Hashing trick. B. Pure log transform. C. Add interactions/powers of features. D. Discretization into bins.

Select an answer to check.

Answer: Add interactions/powers of features.

The best option here is Add interactions/powers of features.. Increases model expressiveness. This is the most accurate statement for how is polynomial features best characterized. The other options are either incomplete or contextually incorrect.

Q49. Which option best describes binning/discretization?

A. Pure normalization. B. Convert continuous to categorical bins. C. Z-score standardization. D. Polynomial features.

Select an answer to check.

Answer: Convert continuous to categorical bins.

For this question, Convert continuous to categorical bins. is correct. Helpful for tree models sometimes. This is the most accurate statement for which option best describes binning/discretization. The other options are either incomplete or contextually incorrect.

Q50. What is the primary purpose of binning/discretization?

A. Z-score standardization. B. Pure normalization. C. Convert continuous to categorical bins. D. Polynomial features.

Select an answer to check.

Answer: Convert continuous to categorical bins.

Convert continuous to categorical bins. is the correct answer here. Helpful for tree models sometimes. This is the most accurate statement for what is the primary purpose of binning/discretization. The other options are either incomplete or contextually incorrect.