Zomato Data Scientist Interview Questions and Answers

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Q1. Count number of Parameters in BERT
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Q2. 3 sum array problem
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Q1. Explain all of decision tree and random forest?
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Decision tree is a tree-like model of decisions and their possible consequences, while random forest is an ensemble learning method that builds multiple decision trees and merges them together.
• Decision tree is a flowchart-like structure where each internal node represents a decision based on an attribute, each branch represents the outcome of the decision, and each leaf node represents a class label.

• Random forest is a ...read more
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Q2. Some basic probability questions
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Q1. What do you know about Uber?
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Q2. Went over my current role
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Q1. Explain past projects
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Q1. Explain resume points
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Resume points are concise descriptions of your work experience, skills, and achievements listed on your resume.
• Resume points should be clear, specific, and quantifiable.

• Use action verbs to start each point, such as 'developed', 'implemented', 'analyzed'.

• Include relevant metrics or results to demonstrate impact, such as 'increased sales by 20%' or 'reduced processing time by 30%'.
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Q1. Write a SQL query to find all duplicate emails in a table named person
Ans. 
SQL query to find duplicate emails in a table named person
• Use GROUP BY and HAVING clause to group emails and count their occurrences

• Select only those emails which have count greater than 1

• Example: SELECT email, COUNT(*) FROM person GROUP BY email HAVING COUNT(*) > 1;
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Q2. Given a weather table, write a sql query to find all date's ids with higher temperature compared to it's previous dates
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SQL query to find date ids with higher temperature compared to previous dates in weather table
• Use self join to compare temperature of current date with previous dates

• Order the table by date to ensure correct comparison

• Select date ids where temperature is higher than previous dates
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Q1. Ask 4 programming question 1)prime in range ,2)Matrix multiplication,3)Spiral Matrix 4)separte vowels from string
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Q1. Explain bagging vs boosting
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Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previ...read more
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How to judge if the comment is actually describing a product