Exl India Interview Questions, Process, and Tips

Q1. Basic accounting questions.
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Q2. Ms Excel related questions
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Q1. Basic Information about yourself and Communication
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Q2. Some knowledge about work
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Q1. Question related to the filed
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Q2. Situational based question
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Q1. What are the factors you would consider for a CLI?
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Q2. How will you assess the performance of a credit risk model?
Ans. 
Assessing credit risk model performance involves evaluating its accuracy, reliability, and effectiveness in predicting creditworthiness.
• Evaluate the model's accuracy by comparing predicted credit risk with actual outcomes.

• Assess the model's reliability by testing its consistency over time and across different datasets.

• Examine the model's effectiveness in predicting creditworthiness by analyzing its ability to different...read more
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Q3. How will you simulate the stock prices using Monte Carlo and GBM?
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Simulating stock prices using Monte Carlo and GBM involves generating random paths based on historical data and volatility.
• Generate random paths based on historical stock prices and volatility using Monte Carlo simulation

• Use Geometric Brownian Motion (GBM) to model the stochastic process of stock prices

• Calculate the drift and volatility of the stock using historical data

• Simulate multiple future stock price paths by ite...read more
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Q1. Basic questions on salary etc.
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Case study related to marketing

Q1. Window sum in sql
Ans. 
Window sum in SQL is a way to calculate the sum of a specific column over a specified window of rows.
• Use the OVER clause with the SUM function to calculate window sum.

• Specify the window frame using ROWS or RANGE keyword.

• Example: SELECT column_name, SUM(column_name) OVER (ORDER BY column_name ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS window_sum FROM table_name;
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Q2. Tableau related questions
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Q1. Expectations and general hr queries
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Q1. Design an analytical framework for IPL bidding sustem
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An analytical framework for IPL bidding system
• Identify key performance indicators (KPIs) such as player performance, team performance, market value, etc.

• Collect and analyze historical data on player and team performance, auction prices, and market trends

• Develop predictive models using machine learning algorithms to forecast player and team performance and auction prices

• Use the models to simulate different bidding scena...read more
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Q1. Case studies on data science and analytics
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Q1. Cultural fitment and managerial
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Q1. Project details and cultural fitment
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