AML RightSource Associate Interview Questions and Answers

Q1. About the final year project for freshers
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Q2. Self introduction and questions from resume
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Q1. Basic technology
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Q1. What is nav describe
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NAV stands for Net Asset Value, which represents the per-share value of a mutual fund or exchange-traded fund (ETF).
• NAV is calculated by subtracting a fund's liabilities from its assets and dividing by the number of shares outstanding.

• It is used to determine the price at which investors can buy or sell shares of the fund.

• NAV is typically calculated at the end of each trading day.

• For example, if a mutual fund has assets...read more
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Q2. What is the function of custodian bank
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Custodian banks are financial institutions responsible for safeguarding assets, such as securities and cash, on behalf of institutional clients.
• Provide safekeeping of assets

• Settle trades on behalf of clients

• Collect income on assets

• Process corporate actions

• Provide reporting and other administrative services

• Act as a liaison between the client and the investment manager
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Some mcq questions and coding question. Both where easy to medium level. Prepare combination/permutation/Time complexity etc

A question related to Binary search and some other follow ups.

Q1. Why did we hired you
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Q2. What is next 5 years see your self
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Q1. Explain random forest
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Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q2. What is boosting
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Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
• Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.

• Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.

• Boosting is used to improve the accuracy of models and reduce bias and variance.
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General qunti, oops questions asked

Q1. Oops concepts, DBMS questions, data structures
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