SoftwareONE Associate Consultant Interview Questions and Answers

Q1. Java related questions
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Q1. Selenium related questions
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Q1. Exceptions and thread implementation
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Q2. Basics of restful web services
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There were 2 coding questions asked. If one question is solved you are qualified.

Q1. There are 3 technical interviews. First tech interview is simple. Then the difficulty arise in further rounds.
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Q1. They ask to solve the coding question with different logic.
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General Excel Test an Easy to understand

Q1. General question
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Q2. Package related questions
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Q1. What is Accruals
Ans. 
Accruals are expenses incurred but not yet paid or recorded.
• Accruals are expenses that have been incurred but not yet paid for.

• They are recorded in the financial statements to reflect the true financial position of a company.

• Accruals help match expenses with revenues in the same accounting period.

• Examples include accrued salaries, interest, and taxes.

• Accruals are necessary for accurate financial reporting.
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Q2. What prepaid recalss
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Prepaid recalls are prepaid expenses that are reversed in the subsequent accounting period.
• Prepaid recalls are expenses that have been paid in advance but are recognized as expenses in the following accounting period.

• They are typically recorded as assets on the balance sheet until they are recognized as expenses.

• Common examples of prepaid recalls include prepaid rent, insurance premiums, and subscription fees.
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Q1. Explain random forest
Ans. 
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
Ans. 
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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