Bristlecone Associate Interview Questions and Answers

It was a Aptitude,Reasoning, English and coding based round.

Q1. Tell me about yourself.
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Q2. Tell me about yourself.
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Q3. Tell me about yourself.
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Q4. Tell me yourself ,your project,which language you know,differentiate between them,reverse an array,what is linked list,what double linkedlist,what is ASCII,how efficient you are in SQL,what rdbms,truncate ...read more
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Q5. It's a round of 40 mins
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Q1. Tell me about yourself and your project
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Q2. General knowledge questions.15 mins round.
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Q1. What are your salary expectations?
Ans. 
I am looking for a competitive salary that reflects my skills and experience, ideally in the range of $X to $Y.
• Research industry standards: For example, according to Glassdoor, the average salary for this role is $X.

• Consider my experience: With X years in the field, I bring valuable skills that justify a higher salary.

• Be open to negotiation: I am flexible and willing to discuss a compensation package that includes bene...read more
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Q2. What is your family background?
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Q3. Why should we hire you?
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Q4. Where do you see yourself in 5 years?
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Q5. What are your strengths and weaknesses?
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Q6. Tell me about yourself.
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Aptitude test was conducted at first

Q1. One-on-one round in the interview process
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Q1. HR round was interactive and friendly.
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Q1. Few technical questions were asked in round 4
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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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Q1. Technical terms from jd
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Q1. Behaviour and previous company related questions
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