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I applied via Company Website and was interviewed before May 2023. There were 2 interview rounds.
Nothing normal intro work project self
FMCG brand manufacturers produce a wide range of categories including food, beverages, personal care, household products, and more.
Food products
Beverages
Personal care items
Household products
Health and wellness products
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posted on 11 Dec 2024
I applied via LinkedIn and was interviewed in Nov 2024. There were 2 interview rounds.
There are 10 multiple-choice questions (MCQs) on Python, 20 MCQs on machine learning (ML), and 10 questions on deep learning (DL).
I applied via Campus Placement and was interviewed in Dec 2024. There were 4 interview rounds.
Two coding questions related to matrices and heaps.
I applied via Naukri.com and was interviewed in Dec 2024. There were 4 interview rounds.
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I applied via Naukri.com and was interviewed in Oct 2024. There were 2 interview rounds.
I applied via Naukri.com and was interviewed in Dec 2024. There were 2 interview rounds.
It was well designed
They will ask aptitude, reasoning and other questions as an online test to know your problem solving skills.
They will give a topic where either we have to speak or write on that topic
ALL() ignores all filters in the query context, while ALLSELECTED() ignores only filters on columns in the visual.
ALL() removes all filters from the specified column or table.
ALLSELECTED() removes filters from the specified column or table, but keeps filters on other columns in the visual.
Example: ALL('Table') would remove all filters on the 'Table' in the query context.
Example: ALLSELECTED('Column') would remove filte...
COUNT() counts only numeric values, while COUNTA() counts all non-empty cells.
COUNT() counts only cells with numerical values.
COUNTA() counts all non-empty cells, including text and errors.
Example: COUNT(A1:A5) will count only cells with numbers, while COUNTA(A1:A5) will count all non-empty cells.
Developed a predictive model to forecast sales based on historical data
Collected and cleaned historical sales data
Performed exploratory data analysis to identify trends and patterns
Built and trained a machine learning model using regression techniques
Evaluated model performance using metrics like RMSE and MAE
Interview experience
based on 2 reviews
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