SBI Cards & Payment Services Data Scientist Interview Questions and Answers

Many Mcq,s.Similar to cat exam

Ml case study . Eg loan default prediction

Q1. What do you do if your model has higher false positives
Ans. 
Investigate the model performance metrics and adjust the threshold for classification.
• Analyze the confusion matrix to understand the distribution of false positives.

• Adjust the threshold for classification to reduce false positives.

• Consider using different evaluation metrics like precision, recall, and F1 score.

• Explore feature importance to identify variables contributing to false positives.
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Q1. Explain previous projects
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Q2. Explain 2 algo of your choice
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Medium General Aptitude questions and technical(Big Data, Python etc.)

Q1. ML Algorithms (SVM, Random forest, bagging boosting, ridge, etc)
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Q1. Deep equations and understading of DL and ML Algorithms
Ans. 
Understanding deep equations and algorithms in DL and ML is crucial for a data scientist.
• Deep learning involves complex neural network architectures like CNNs and RNNs.

• Machine learning algorithms include decision trees, SVM, k-means clustering, etc.

• Understanding the math behind algorithms helps in optimizing model performance.

• Equations like gradient descent, backpropagation, and loss functions are key concepts.

• Practica...read more
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Hard level aptitude questions... Time taking sums

Q1. Puzzles and probability questions
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Q2. Asking completely about data science projects in your resume
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Q1. Just asked some puzzle questions
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Q2. Asked if iam willing to relocate and about my family
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Aptitude test for about an hour.

Q1. Questions related to data science were asked based on your resume. Puzzles were asked too. Mostly they were from gfg.
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Q2. What are parameters used in a random forest.
Ans. 
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
• Number of trees: The number of decision trees to be used in the random forest.

• Maximum depth of trees: The maximum depth allowed for each decision tree.

• Minimum samples split: The minimum number of samples required to split a node.

• Maximum features: The maximum number of features to consider when
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