Mjunction Services Manager Interview Questions and Answers

Q1. Share your experience
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Q2. Last CTC drawn from company
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I drew a CTC of INR 10 lakhs per annum in my previous company.
• My last CTC was INR 10 lakhs per annum.

• I received a salary of INR 83,333 per month.

• This included my basic salary, allowances, and other benefits.

• I am open to discussing my salary expectations for this role.
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Prime numbers,reverse a string

Q1. DSA, python,java questions
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Q1. Company policy,work culture
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Q1. About the final year project for freshers
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Q2. Self introduction and questions from resume
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General qunti, oops questions asked

Q1. Oops concepts, DBMS questions, data structures
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Q1. Be express about youself
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Q1. Explain technical skills
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Technical skills refer to the abilities and knowledge needed to perform specific tasks in a professional field.
• Technical skills are practical abilities that are gained through training, education, and experience.

• They are specific to a particular job or industry and are often related to using tools, software, or equipment.

• Examples of technical skills include programming languages, data analysis, graphic design, and proj...read more
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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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