Chubb Senior Data Associate Interview Questions and Answers

LSTM RNN is a type of RNN that can learn long-term dependencies, while simple RNN struggles with vanishing/exploding gradients.

• LSTM RNN has more complex architecture with memory cells, input, forget, and output gates.

• Simple RNN has a single tanh activation function and suffers from vanishing/exploding gradients.

• LSTM RNN is better at capturing long-term dependencies in sequences.

• Simple RNN is simpler but struggles with

Lasso regression is a type of linear regression that uses L1 regularization to prevent overfitting by adding a penalty term to the loss function.

• Lasso regression helps in feature selection by shrinking the coefficients of less important features to zero.

• It is particularly useful when dealing with high-dimensional data where the number of features is much larger than the number of samples.

• The regularization parameter in...read more

Fine tuning a LLM model involves adjusting hyperparameters to improve performance.

• Perform grid search or random search to find optimal hyperparameters

• Use cross-validation to evaluate different hyperparameter combinations

• Regularize the model to prevent overfitting

• Adjust learning rate and batch size for better convergence

• Consider using techniques like early stopping to prevent overfitting

Deep learning is used over statistical models for complex, non-linear relationships in data.

• Deep learning can automatically learn hierarchical representations of data, capturing intricate patterns and relationships.

• Statistical models may struggle with high-dimensional data or non-linear relationships, where deep learning excels.

• Deep learning can handle unstructured data like images, audio, and text more effectively tha...read more

XGB is better than RF due to its ability to handle complex relationships and optimize performance.

• XGB uses gradient boosting which allows for better handling of complex relationships compared to RF

• XGB optimizes performance by using regularization techniques to prevent overfitting

• XGB is faster and more efficient in training compared to RF

• XGB allows for parallel processing which can speed up computation

• XGB has been shown...read more

Feature engineering is the process of selecting and transforming relevant features from raw data to improve model performance.

• Identify relevant features based on domain knowledge and data exploration

• Transform features to improve their quality and relevance

• Create new features by combining or extracting information from existing features

• Select the most important features using feature selection techniques

• Iterate the proc

I have used logistic regression and decision tree models for classification.

• Logistic regression is a linear model used for binary classification.

• Decision tree is a non-linear model used for multi-class classification.

• Logistic regression is simple and easy to interpret while decision tree can handle non-linear relationships.

• I chose these models based on the nature of the data and the problem at hand.

Tableau Dashboard actions allow users to interact with the data and visualizations by clicking on specific elements.

• Dashboard actions can be used to filter data, highlight specific data points, or navigate to other dashboards.

• There are four types of actions in Tableau: filter, highlight, URL, and parameter.

• For example, a user can click on a bar chart to filter the data in a related table or click on a map to highlight ...read more