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I applied via Walk-in and was interviewed before Apr 2023. There was 1 interview round.
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I applied via campus placement at National Institute of Technology (NIT), Calicut and was interviewed in Nov 2024. There were 2 interview rounds.
"I applied via campus placement First round was an aptitude containing quant,english, logical reasoning and data interpretation (60 mins ,60 questions) plus technical round (30 mins,30 questions) conducted via Aon platform. Aptitude was easy but was a bit lengthy. Technical test was also moderate and covered all topics from mechanical core. After the written test there was a resume shortlisting for people who cleared round one.Prepare ur resume according to the role ur applying. From around 87 members 27 were shortlisted for GD round. ."
They divided us into 4 groups and group discussion went on for around 20 mins my topic was (Are ev vehicle's an efficient solution for pollution). In gd round listen to the topic carefully. Don't hesitate to put ur thoughts first donot interfere while other is speaking listen to them and put ur thoughts in a politeful way , mostly stick to the topic. After 20 mins the recruiters asked me to summarise the topic and i gave a brief summary keeping everyone's and my thoughts in mind. After GD 9 members were selected for the interview.Gd and interview happened on the same day.
I applied via Job Portal and was interviewed in Dec 2024. There was 1 interview round.
I applied via Referral and was interviewed in Sep 2024. There was 1 interview round.
I applied via Company Website and was interviewed in Jun 2024. There were 2 interview rounds.
A medium python program
Optimizing code for better performance and efficiency
Use built-in functions and libraries for faster execution
Minimize unnecessary loops and conditions
Avoid redundant code and optimize data structures
Implement caching or memoization for repetitive computations
Optimizing Python code involves improving efficiency and performance.
Use built-in functions and libraries instead of writing custom code
Avoid unnecessary loops and nested loops for better performance
Optimize data structures and algorithms for faster execution
Numpy is a powerful library for numerical operations in Python, with efficient array operations and mathematical functions.
Use vectorized operations instead of loops for better performance.
Avoid unnecessary copying of arrays to save memory.
Utilize broadcasting to perform operations on arrays of different shapes.
Use numpy functions like np.sum(), np.mean(), np.max(), etc. for efficient calculations.
Optimize code by prof
posted on 29 Nov 2024
I am a trainee at XYZ Company, currently working on various projects under the guidance of senior team members.
Trainee at XYZ Company
Working on various projects
Under the guidance of senior team members
I am a dedicated and hardworking individual with a passion for learning and growth.
I have a strong work ethic and am always eager to take on new challenges.
I am a quick learner and adapt well to new environments.
I am detail-oriented and strive for excellence in everything I do.
I applied via Recruitment Consulltant and was interviewed in Aug 2024. There was 1 interview round.
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