Goldman Sachs Associate Interview Questions, Process, and Tips

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
Ans. 
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
Ans. 
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. Company information and self introduction in brief
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Q2. Complete technical questions and process related knowledge was tested
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Q1. Major accomplishments and situational questions to test leadership qualities
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Q2. Tested rigorously by giving managerial scenarios
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Q1. Why Goldman Sachs and what makes you leave Morgan Stanley
Ans. 
I chose Goldman Sachs for its global reach and diverse opportunities. I left Morgan Stanley to pursue new challenges and growth.
• Goldman Sachs offers a wider range of global opportunities compared to Morgan Stanley

• I was seeking new challenges and growth that I believed Goldman Sachs could provide

• I was attracted to the diverse and innovative culture at Goldman Sachs
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Q2. How will you handle a team with no experience previously and how will you deal with credit card again as no experience on the same.
Ans. 
I will provide training and guidance to help the team learn and adapt to handling credit card transactions.
• Provide thorough training on credit card processing procedures

• Offer guidance and support as team members gain experience

• Implement regular check-ins and feedback sessions to address any challenges

• Encourage open communication and a collaborative team environment

• Utilize resources such as online tutorials or industry
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Q1. This was with an MD and India Head
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Q2. Just testing character as human and the qualities that you possess for a role of this stature
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Q1. Questions were based on my resume and JD
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Q2. Resume and JD based
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Q1. Based on resume and JD
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Q1. Scenario based question
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Q1. Based on resume
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