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Quantitative, Qualitative
I applied via Job Portal
There is some technical questions in that and some of them aptitude like a based on statistics and mathematics and sum of them comprehensive questions and some of them English logical questions like reasoning
Inner join combines rows from two tables based on a related column between them.
Inner join returns only the rows where there is a match between the columns in both tables
Null values in the columns being joined will not affect the inner join result
Blank values or non-matching values will not be included in the inner join result
I applied via Referral and was interviewed in Mar 2023. There were 4 interview rounds.
45 different questions to be complete in 1 hr
I applied via Company Website and was interviewed before Jan 2024. There were 3 interview rounds.
Aptitude questions covering English, Mathematics, Python, and SQL.
I applied via LinkedIn and was interviewed in Feb 2023. There were 3 interview rounds.
Maths, basic python and sql questions, English and basic stats test
Top trending discussions
NER training using deep learning
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
Break down the assignment into smaller tasks to make it more manageable
Set deadlines for each task to stay on track
Regularly check progress to ensure everything is on schedule
Seek feedback from colleagues or supervisors to improve the quality of work
I applied via Recruitment Consulltant and was interviewed in Oct 2024. There was 1 interview round.
I applied via Job Fair and was interviewed in May 2024. There were 3 interview rounds.
They gave a span of 3 days to build an AI-powered webapp
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
Experience in setting up and managing virtual machines, storage, and networking in cloud environments
Knowledge of cloud services like EC2, S3, RDS, and Lambda
Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
Developed a predictive model for customer churn in a telecom company
Collected and cleaned customer data from various sources
Performed exploratory data analysis to identify key factors influencing churn
Built and fine-tuned machine learning models to predict customer churn
Challenges included imbalanced data, feature engineering, and model interpretability
I applied via Approached by Company and was interviewed in May 2024. There were 3 interview rounds.
DSA was asked. And general coding language questions were asked. Previous experience based questions were asked.
Machine Learning, Generative AI, Deep learning interview questions. 2 Coding problems based on Algorithms.
1 Interview rounds
based on 2 reviews
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