CRED Data Analyst Interview Questions and Answers

Basic python, sql and pandas questions

Q1. Case study (Probability questions)
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Q2. Resume projects
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Hackerrank test mostly on python and sql

Q1. SQL based questions
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Q2. Python based questions
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Q1. Basic SQL questions are asked in this round Some theory questions are asked as well
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Q1. Types of Window Function
Ans. 
Window functions are used in SQL to perform calculations across a set of table rows related to the current row.
• Types include ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE(), LAG(), LEAD(), FIRST_VALUE(), LAST_VALUE(), etc.

• They allow for calculations to be performed on a specific subset of rows within a query result set.

• Window functions are commonly used for running totals, moving averages, and ranking data.
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Q2. Difference between Joins with examples
Ans. 
Joins are used to combine rows from two or more tables based on a related column between them.
• Inner Join: Returns rows when there is a match in both tables.

• Left Join: Returns all rows from the left table and the matched rows from the right table.

• Right Join: Returns all rows from the right table and the matched rows from the left table.

• Full Outer Join: Returns rows when there is a match in one of the tables.

• Cross Join:
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Technical SQL Coding Round of 1 hour

Began with 2 easy generic question -
1. No. of record on different types of joins.
2. Employees who have higher salaries than their managers,

Then moved to medium questions -
1. Implement Rolling sum with & without window functions.
2. Other questions were on CTEs, Joins & other complex queries.

Bar Raiser Round of 1 hour involving -

1. Case study: How can we leverage product knowledge & analytics to increase Amazon Prime Revenue from 10% to 12%?

2. Metrics awareness: Common metrics discussion like DAU, PDAU, MAU, Retention, Churn, etc.

3. Resume based work exp discussion

Q1. Quick culture fitment round with HR discussing about past work experience, my expectations & why I want to join Jupiter.
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Intermediate level coding round

Q1. Project explaination
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Q2. Explain project ind detail
Ans. 
Developed a predictive analytics model to forecast customer churn for a telecommunications company.
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify patterns and trends

• Built a machine learning model using logistic regression to predict customer churn

• Evaluated the model's performance using metrics such as accuracy, precision, and recall
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Round duration - 90 Minutes
Round difficulty - Easy

Problem solving using SQL questions

Round duration - 90 Minutes
Round difficulty - Easy

A marketing campaign is run, how will you decide metrics to be tracked. Techincaly a KPI round

Q1. In a marketing campaign, how would you decide which metrics to track?
Ans. 
Metrics selection based on campaign objectives, target audience, and key performance indicators.
• Identify campaign objectives and goals

• Consider target audience and their behavior

• Select key performance indicators (KPIs) relevant to the campaign

• Track metrics such as conversion rate, click-through rate, ROI, customer acquisition cost

• Analyze data to measure success and make data-driven decisions
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Round duration - 30 Minutes
Round difficulty - Easy

Social empathatic fit round

How to increase personal insurance revenue to 5x?