Akanksha

Posted on Oct 30, 2023

Top 30 Automated machine learning Interview Questions with Answers

1. What does AutoML stand for?

a) Automated Machine Learning
b) Artificial Model Learning
c) Automated Model Language
d) Auto Mechanism Language
Answer: a) Automated Machine Learning

2. Which of the following is not a typical task for AutoML?

a) Data Preprocessing
b) Model Selection
c) Feature Engineering
d) Manual Hyperparameter Tuning
Answer: d) Manual Hyperparameter Tuning

3. AutoML can automate which stage of the machine learning pipeline?

a) Data Collection
b) Model Deployment
c) Model Evaluation
d) All of the above
Answer: c) Model Evaluation

4. What is the goal of automated feature engineering in AutoML?

a) Reduce the number of features
b) Increase the model's interpretability
c) Create new features from existing data
d) Improve the data collection process
Answer: c) Create new features from existing data

5. Which algorithm is commonly used for hyperparameter optimization in AutoML?

a) K-Nearest Neighbors
b) Random Forest
c) Gradient Boosting
d) Bayesian Optimization
Answer: d) Bayesian Optimization

6. Which AutoML library is known for its simplicity and ease of use?

a) TensorFlow
b) PyTorch
c) scikit-learn
d) H2O.ai
Answer: c) scikit-learn

7. What is the primary purpose of model stacking in AutoML?

a) Reducing model complexity
b) Combining the predictions of multiple models
c) Improving data preprocessing
d) Feature selection
Answer: b) Combining the predictions of multiple models

8. Which technique helps prevent overfitting in AutoML?

a) Cross-Validation
b) Feature Scaling
c) Data Imputation
d) Regularization
Answer: a) Cross-Validation

9. What is the F1-score used for in AutoML?

a) Model training time
b) Model interpretability
c) Model performance evaluation
d) Model deployment
Answer: c) Model performance evaluation

10. Which AutoML tool is developed by Google and often used for Kaggle competitions?

a) Auto-Sklearn
b) TPOT
c) AutoGluon
d) AutoML Tables
Answer: d) AutoML Tables

11. What is the purpose of hyperparameter tuning in AutoML?

a) Feature selection
b) Optimizing model parameters
c) Data preprocessing
d) Model deployment
Answer: b) Optimizing model parameters

12. Which AutoML technique is designed to handle imbalanced datasets?

a) Data augmentation
b) Oversampling
c) Feature selection
d) Model stacking
Answer: b) Oversampling

13. Which AutoML method is based on genetic algorithms?

a) Random Search
b) Grid Search
c) TPOT
d) H2O.ai
Answer: c) TPOT

14. What does the term "ensemble learning" mean in AutoML?

a) Training multiple models on the same data
b) Combining predictions from multiple models
c) Training models with large ensembles of features
d) Using deep learning models exclusively
Answer: b) Combining predictions from multiple models

15. In AutoML, what does the term "One-Hot Encoding" refer to?

a) A technique for optimizing neural networks
b) A data preprocessing technique for categorical variables
c) A method for imputing missing data
d) A model evaluation metric
Answer: b) A data preprocessing technique for categorical variables

16. What is the purpose of cross-validation in AutoML?

a) Avoiding underfitting
b) Reducing the number of features
c) Evaluating model performance on unseen data
d) Feature engineering
Answer: c) Evaluating model performance on unseen data

17. Which AutoML tool is known for its deep learning capabilities?

a) Auto-Sklearn
b) AutoGluon
c) TPOT
d) H2O.ai
Answer: b) AutoGluon

18. Which metric is commonly used to evaluate classification models in AutoML?

a) Mean Absolute Error (MAE)
b) R-squared (R2)
c) Accuracy
d) Root Mean Squared Error (RMSE)
Answer: c) Accuracy

19. Which AutoML library is specifically designed for time series forecasting?

a) Auto-Sklearn
b) TPOT
c) AutoGluon
d) Prophet
Answer: d) Prophet

20. What does "bias-variance trade-off" refer to in AutoML?

a) The balance between feature engineering and model selection
b) The trade-off between model complexity and model accuracy
c) The trade-off between data preprocessing and model evaluation
d) The trade-off between underfitting and overfitting
Answer: d) The trade-off between underfitting and overfitting

21. Which AutoML approach uses reinforcement learning to optimize models?

a) Auto-Sklearn
b) TPOT
c) AutoGluon
d) AutoRL
Answer: d) AutoRL

22. What is the primary goal of AutoML?

a) Automate all aspects of machine learning
b) Eliminate the need for human intervention in machine learning
c) Improve model performance without human bias
d) Replace data scientists and machine learning engineers
Answer: c) Improve model performance without human bias

23. Which AutoML tool is known for its capability to handle structured and tabular data?

a) Auto-Sklearn
b) AutoGluon
c) TPOT
d) H2O.ai
Answer: b) AutoGluon

24. Which of the following is NOT a typical AutoML technique?

a) Hyperparameter tuning
b) Model evaluation
c) Algorithm development
d) Feature engineering
Answer: c) Algorithm development

25. Which AutoML approach is designed to search for the best combination of preprocessing steps and models?

a) Auto-Sklearn
b) TPOT
c) AutoGluon
d) H2O.ai
Answer: b) TPOT

26. What is the primary purpose of AutoML libraries like Auto-Sklearn?

a) Automating the entire machine learning process
b) Replacing the need for data scientists
c) Streamlining data collection
d) Simplifying model selection and hyperparameter tuning
Answer: d) Simplifying model selection and hyperparameter tuning

27. In AutoML, what is the role of the "meta-learner" in model stacking?

a) The primary model that makes predictions
b) The model that selects the best features
c) The model that combines predictions from base models
d) The model that handles missing data
Answer: c) The model that combines predictions from base models

28. Which AutoML technique is used to handle missing data in datasets?

a) One-Hot Encoding
b) Data Augmentation
c) Data Imputation
d) Feature Scaling
Answer: c) Data Imputation

29. Which AutoML approach uses a genetic algorithm to search for the best model and hyperparameters?