Goldman Sachs Associate Interview Questions, Process, and Tips

Q1. String manipulation with two pointers related question was asked
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Q1. Gas and track problem
Ans. 
The gas and track problem involves determining the amount of gas required to complete a track.
• Calculate the distance of the track

• Determine the fuel efficiency of the vehicle

• Divide the distance by the fuel efficiency to get the required amount of gas
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Q2. Java design patterns
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Difficulty level - medium

Q1. How to improve model performance beyond hyperopt?
Ans. 
To improve model performance beyond hyperopt, consider ensemble methods, feature engineering, and data augmentation.
• Implement ensemble methods like bagging, boosting, or stacking to combine multiple models.

• Perform feature engineering to create new informative features or transform existing ones.

• Apply data augmentation techniques to increase the size and diversity of the training data.

• Consider using advanced algorithms ...read more
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Q2. Difference between xgboost and gbm
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XGBoost is an optimized version of Gradient Boosting Machine (GBM) with additional features and improvements.
• XGBoost is a scalable and efficient implementation of gradient boosting algorithm.

• XGBoost uses a more regularized model formalization to control overfitting.

• XGBoost supports parallel processing and can handle large datasets.

• XGBoost provides built-in regularization techniques like L1 and L2 regularization.

• XGBoost...read more
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Q3. How to tune the model performance?
Ans. 
To tune model performance, adjust hyperparameters, preprocess data, increase training data, and use ensemble methods.
• Adjust hyperparameters such as learning rate, regularization, and batch size.

• Preprocess data by scaling, normalizing, or handling missing values.

• Increase training data to reduce overfitting and improve generalization.

• Use ensemble methods like bagging or boosting to combine multiple models.

• Perform cross-v...read more
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Q4. How to handle class imbalance
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Handling class imbalance involves techniques such as resampling, adjusting class weights, and using ensemble methods.
• Resampling techniques like oversampling the minority class or undersampling the majority class can help balance the classes.

• Adjusting class weights during model training can give more importance to the minority class.

• Using ensemble methods like bagging or boosting can improve the performance on imbalance...read more
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Q1. CV based questions were asked
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Q2. Work experience based questions were asked
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Q1. Details about my work in depth
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Q2. Details about my experience
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Q1. About me and my work experience
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Q2. Details about my work experience and expectations
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