Altimetrik Talent Acquisition Specialist Interview Questions and Answers

Q1. Goals in near future
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Q2. Where do you see yourself
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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.

They gave one topic and I was interviewed with 7 other candidates. The topic is easy.

Q1. The interviewer asked general questions and basically analyse the communication skills
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Q2. Tell me about your family background
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Q1. HR asked general questions about family, life and about the job role.
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Q1. I was asked to prepare a presentation and questions were asked based on it and some other questions are asked.
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30mins on Verbal, QA and Logiacal Reasoning

Topic will be given for GD

Q1. Tell me about yourself
Ans. 
Experienced Talent Acquisition Specialist with a passion for connecting top talent with leading organizations.
• Over 5 years of experience in full-cycle recruitment

• Strong expertise in sourcing, interviewing, and negotiating offers

• Proven track record of successfully filling positions in various industries

• Passionate about building relationships and finding the perfect fit for both candidates and clients
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Q2. Why do you want to join Collabera?
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I want to join Collabera because of its reputation for providing excellent career growth opportunities and a supportive work environment.
• Collabera has a strong reputation in the industry for providing opportunities for career growth and development.

• I am impressed by Collabera's commitment to fostering a supportive and inclusive work environment.

• I believe that Collabera's values align with my own professional goals and ...read more
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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
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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