Hitachi Digital Services Associate Interview Questions and Answers

A bit on the tougher side

Q1. A few questions based on JD
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Q1. Total analysis of candidate
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Total analysis of candidate
• Assessing candidate's skills, experience, and qualifications

• Evaluating candidate's personality, communication skills, and work ethic

• Reviewing candidate's references and background check

• Conducting interviews and assessments to determine fit for the role

• Considering candidate's potential for growth and development
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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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Java basic coding with collections

Q1. Questions on Java
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Q1. Behavioral questions
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