Brillio Team Lead Interview Questions and Answers for Experienced

Q1. Speak something about your self
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Q2. Explanation on general programming language I have learnt
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Q3. Questions about OOPs andJava
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Q1. Core Java question and grilling on that
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Q2. Write code binary searching algorithm.
Ans. 
Binary search algorithm efficiently finds the target value in a sorted array.
• Divide array in half and compare target with middle element

• If target is smaller, search left half; if larger, search right half

• Repeat process until target is found or subarray is empty
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Q3. Give example of any design pattern
Ans. 
One example of a design pattern is the Singleton pattern.
• Singleton pattern ensures a class has only one instance and provides a global point of access to it.

• It involves a static method to access the instance and a private constructor to prevent instantiation from other classes.

• Example: Singleton pattern is commonly used in logging classes, database connections, and thread pools.
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Hackerrank coding test

Q1. Java springboot java 8 angular
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Q1. Tell me about yourself.
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Questions on Aws services

Q1. Aws Services EC2
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Q1. Why you relieved previous organisation?
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Q1. Are you immediate joiner?
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45 mins, maths

Q1. What are your salary expectations?
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Q2. Share details of your previous job.
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Q3. Tell me about yourself.
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Q1. Question about Java 8
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Q2. Questions about Micro services
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Q1. Salary discussion
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Q2. Documentation and salary negotiation
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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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