Hitachi Digital Services Associate SAP Consultant Interview Questions and Answers

Q1. Exceptions and thread implementation
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Q2. Basics of restful web services
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Q1. Ry fy fg gy hh
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Q1. Dep inj authenticate authorizer ques
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Q1. Dep inj aith, c#, aws, .net core
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Q1. Related to MDM and IICs And ETL and SQL
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Q1. About your self, qualification, skills
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Q1. Previous experience, Roles and responsibilities
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Q1. Why you want change your job.
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CET level questions, Math, Comprehension

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. What is SAP basis
Ans. 
SAP Basis is the technical foundation that enables SAP applications to function.
• SAP Basis includes the administration of the SAP system, database, and operating system.

• It involves tasks such as system monitoring, performance tuning, and troubleshooting.

• SAP Basis consultants are responsible for ensuring the smooth running of SAP applications.

• Examples of SAP Basis tasks include system upgrades, patch management, and user
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Q1. What is your Notice period
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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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