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I applied via Recruitment Consulltant and was interviewed before Mar 2023. There were 3 interview rounds.
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I applied via campus placement at Lovely Professional University (LPU) and was interviewed in Sep 2022. There were 4 interview rounds.
It will be the basic aptitude test .
It would be the technical test where you will be having some output based questions and some coding questions.
I applied via campus placement at College of Engineering ( Formerly Pune Instiute of Enginering and Technology ), Pune and was interviewed in Nov 2024. There were 2 interview rounds.
Aptitude test contains Logical Reasoning Questions, Analytical Skills questions, and some general Questions, there are no coding questions for it
I applied via Walk-in and was interviewed in Dec 2024. There were 5 interview rounds.
Logical and reasoning questions.
Introduce the topic area of the report, outlining the purpose of the case study. Summarize the key issues and findings without providing specific details, and identify the theory employed.
I applied via Approached by Company and was interviewed in Nov 2024. There were 2 interview rounds.
I applied via Naukri.com and was interviewed in Dec 2024. There were 3 interview rounds.
I applied via Naukri.com and was interviewed in Nov 2024. There were 4 interview rounds.
Can you recommend any free website for taking an online carrier aptitude test and recieving a detailed report on sutaible job options.
What is the best resource to learn data structure and algorithm(must contain practice assignments too)? I have stumbled across Coursera,but does it cover all essential details about DSA?
I applied via Naukri.com and was interviewed in Nov 2024. There was 1 interview round.
Create calculated fields in Tableau to dynamically adjust to changing table columns.
Use calculated fields to reference specific columns by name instead of position.
Utilize parameters to allow users to select which columns to display.
Consider using custom SQL queries to dynamically adjust to changing table structure.
Developed a predictive model to forecast customer churn for a telecommunications company.
Utilized machine learning algorithms such as logistic regression and random forest
Performed data preprocessing and feature engineering to improve model performance
Collaborated with business stakeholders to understand key drivers of churn
Achieved 85% accuracy in predicting customer churn
based on 1 review
Rating in categories
Trademark Examiner
6
salaries
| ₹5.4 L/yr - ₹5.4 L/yr |
Examiner
5
salaries
| ₹5.4 L/yr - ₹5.4 L/yr |
Trade Marks Examiner
4
salaries
| ₹5.4 L/yr - ₹5.4 L/yr |
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