Mahindra Finance MIS Officer Interview Questions and Answers

Asked about a case study on whom to give a loan among 1000 customers

Q1. It was more related to my projects
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Q2. Asked questions on PCA,KNN and Elbow method .
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Q1. Asked SVM and Logistic regression, their applications and real life uses
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Q2. Matrix formation of a knockout Tournament
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Q1. What is your current role ?
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Q2. Why are you looking for a job change?
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Q1. Python based list iteration, SQL join based questions
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Q1. What are the project you have worked upon ?
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Asked questions from python and SQL

Q1. What are tupples
Ans. 
Tuples are ordered collections of elements, similar to lists but immutable.
• Tuples are created using parentheses ()

• Elements in a tuple can be of different data types

• Tuples are immutable, meaning their elements cannot be changed once created
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Q2. What is credit risk
Ans. 
Credit risk refers to the potential loss that a lender may incur due to a borrower's failure to repay a loan or meet their financial obligations.
• Credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments.

• It is a key consideration for banks and financial institutions when lending money.

• Factors that contribute to credit risk include the borrower's credit history, financi...read more
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Q1. SQL Basics and Good Communication
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Q2. Structured Profile Summary
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Q1. Joins,Where,having
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Normal general aptitude questions

DSA must and sql, python

Pandas and SQL questions and some business case

Q1. The head of the vertical took interview, explained role and asked about my experience.
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Q2. What project did you work on?
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Q1. A senior data scientist asked technical questions on my experience.
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Q2. What is the advantage or lstm over rnn.
Ans. 
LSTM has better memory retention than RNN.
• LSTM can remember long-term dependencies better than RNN.

• LSTM has a gating mechanism that controls the flow of information.

• LSTM can handle vanishing gradient problem better than RNN.

• LSTM is more suitable for tasks that require long-term memory, such as speech recognition and language translation.
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Q3. What is multicollinearity and what are its effects?
Ans. 
Multicollinearity is a phenomenon where two or more independent variables in a regression model are highly correlated.
• It can lead to unstable and unreliable estimates of regression coefficients.

• It can make it difficult to determine the individual effect of each independent variable on the dependent variable.

• It can also result in inflated standard errors and p-values, making it difficult to identify statistically signif...read more
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