PySpark Interview Questions - Solve Practice Problems
Practice the most common PySpark interview questions asked in Data Engineering, Data Analyst and Data Science roles!
Load and Transform Data
Practice loading a CSV file and apply basic transformations such as selecting, filtering, and dropping columns.
Handling Null Values
Clean the dataset by filtering out or replacing null values in various columns.
Calculate Total Purchases by Customer
Group data by customer and compute the total purchase amount per user.
Calculate Discounts on Products
Add a new column calculating discounted prices for products using arithmetic operations.
Load & Transform JSON file
Read a nested JSON file and flatten it using explode and array-handling techniques.
Employees Earning More than Average
Use window functions to find employees whose salary is higher than the department average.
Remove Duplicates From Dataset
Identify and remove duplicate records based on custom logic using window functions.
Word Count Program in PySpark
Implement a word count logic using PySpark RDD transformations on a text file.
Group By and Aggregate List
Group records and aggregate values into lists using advanced group and array functions.
Monthly Transaction Summary
Summarize transactions month-wise by grouping and using date functions to extract months.
Top Players Summary
Generate a summary of top players using joins, aggregations, and string operations.