Databricks ML Associate Feature Engineering Quiz

1. Which approach uses statistical tests to rank features by their importance?

Recursive feature elimination

Univariate feature selection

Tree-based feature importance

Manual selection

2. In Databricks, what is the primary benefit of using MLflow for feature engineering workflows?

Automatic feature selection

Tracking and versioning features and experiments

Eliminating the need for data preprocessing

Guaranteed 100% model accuracy

3. Which feature engineering technique creates interaction terms between multiple variables?

Feature scaling

Feature crossing

Feature normalization

Feature imputation

Submit

5. What does PCA (Principal Component Analysis) do in feature engineering?

Removes outliers from the dataset

Reduces dimensionality while preserving variance

Encodes categorical variables

Fills missing values automatically

6. Which method groups continuous variables into discrete intervals called bins?

Encoding

Binning

Scaling

Imputation

Submit

8. Which technique reduces skewed feature distributions by applying logarithmic or square root transformations?

Normalization

Standardization

Power transformation

Binning

Submit

10. What is the main advantage of using feature hashing in Databricks?

Improved model accuracy

Handling high-dimensional sparse data efficiently

Reducing training time by 50%

Automatic feature scaling

11. Which technique reduces dimensionality by selecting a subset of relevant features for model training?

Feature selection

Feature extraction

Feature scaling

Feature encoding

Submit

13. In Databricks, which DataFrame operation is used to handle missing values?

Drop()

Fillna()

Replace()

Transform()

14. Which method identifies and removes highly correlated features to reduce multicollinearity?

Principal Component Analysis

Correlation analysis

Feature scaling

Cross-validation

Submit

16. What does normalization scale features to range between?

0 and 100

-1 and 1

0 and 1

Mean and standard deviation

17. Which Databricks tool is commonly used for distributed feature engineering?

Apache Spark MLlib

TensorFlow

Scikit-learn

XGBoost

Submit

19. Which imputation method replaces missing values with the mean of existing values?

Forward fill

Mean imputation

K-nearest neighbors

Deletion

20. What is the primary purpose of standardization in feature engineering?

Converting categorical data to numerical

Scaling features to have mean 0 and standard deviation 1

Removing duplicate rows from the dataset

Splitting data into train and test sets

Databricks ML Associate Feature Engineering Quiz

1. Which approach uses statistical tests to rank features by their importance?

2.

What first name or nickname would you like us to use?

2. In Databricks, what is the primary benefit of using MLflow for feature engineering workflows?

3. Which feature engineering technique creates interaction terms between multiple variables?

4. When using Databricks Spark, the ______ estimator is commonly used for scaling multiple features simultaneously.

5. What does PCA (Principal Component Analysis) do in feature engineering?

6. Which method groups continuous variables into discrete intervals called bins?

7. In Databricks ML workflows, Feature Store is used to centralize and manage ______ for reproducibility.

8. Which technique reduces skewed feature distributions by applying logarithmic or square root transformations?

9. The process of creating new features from existing ones is called feature ______.

10. What is the main advantage of using feature hashing in Databricks?

11. Which technique reduces dimensionality by selecting a subset of relevant features for model training?

12. Polynomial features expand the feature space by creating interaction terms and ______ of existing features.

13. In Databricks, which DataFrame operation is used to handle missing values?

14. Which method identifies and removes highly correlated features to reduce multicollinearity?

15. Feature engineering is the process of selecting and transforming raw data into ______ for ML models.

16. What does normalization scale features to range between?

17. Which Databricks tool is commonly used for distributed feature engineering?

18. In feature engineering, one-hot encoding is used to handle ______ variables.

19. Which imputation method replaces missing values with the mean of existing values?

20. What is the primary purpose of standardization in feature engineering?