I applied online. I interviewed at Premium Retail Services (Gurgaon, Haryana) in Jan 2023
Interview
The interview focused on a variety of technical topics, including SQL, Python, and Power BI. The questions were well-structured and tested my knowledge in these areas. I was able to showcase my skills in writing SQL queries, solving problems with Python, and creating data visualizations using Power BI. Overall, the questions were relevant and gave me an opportunity to demonstrate my technical expertise.
Interview questions [1]
Question 1
SQL Questions:
Basic SQL Query:
Write a SQL query to find the second highest salary from an employee table.
JOINs:
What is the difference between an INNER JOIN and a LEFT JOIN? Can you provide an example?
Aggregation Functions:
How would you write a SQL query to calculate the average salary of employees in each department?
Subqueries:
Can you explain what a correlated subquery is and give an example where it might be used?
Optimization:
What strategies would you use to optimize a slow-running SQL query?
Python Questions:
Data Structures:
What are the main differences between a list and a tuple in Python?
Functions:
Write a Python function to reverse a string without using the built-in reverse() method.
Libraries:
How would you use the pandas library to handle missing data in a dataset?
Exception Handling:
What is the purpose of try, except, and finally in Python? Can you provide an example?
Object-Oriented Programming (OOP):
Explain the concept of inheritance in Python and provide a simple example.
Power BI Questions:
Data Transformation:
How would you load data into Power BI from multiple sources and combine them into a single dataset?
DAX Calculations:
What is the difference between calculated columns and measures in Power BI? Can you provide examples of when to use each?
Visualizations:
What types of visualizations are best suited for showing trends over time in Power BI?
Power Query:
How would you use Power Query to clean and transform data before loading it into Power BI?
Performance Optimization:
What steps can you take to improve the performance of a Power BI report with large datasets?