Most Asked Deloitte Data Engineer Interview Questions (Part 3) | LAG, GROUP BY, MIN & Alias

In this third edition of the Most Asked Deloitte Data Engineering Interview Questions, we dive deeper into SQL and pyspark based function questions commonly asked in interviews. From mastering LAG and LEAD to understanding GROUP BY, MIN, and other aggregate functions — we break down each concept with real-world examples.

💡 Topics Covered:
LAG explained
GROUP BY interview tricks
Usage of MIN
Window functions simplified

Perfect for anyone preparing for data engineer roles at Deloitte, TCS, PwC, or any top consulting firm.

Dataset is as follows:

# 1) Product Data
product_data = [
(1, 'Laptops', 'Electronics'),
(2, 'Jeans', 'Clothing'),
(3, 'Chairs', 'Home Appliances')
]

product_schema = ['product_id', 'product_name', 'category']

product_df = spark.createDataFrame(data=product_data, schema=product_schema)
print("Product DataFrame:")
product_df.display()

# 2) Sales Data
sales_data = [
(1, 2019, 1000.00),
(1, 2020, 1500.00),
(1, 2021, 1200.00),
(2, 2019, 500.00),
(2, 2020, 700.00),
(2, 2021, 900.00),
(3, 2019, 400.00),
(3, 2020, 450.00),
(3, 2021, 300.00)
]

sales_schema = ['product_id', 'year', 'total_sales_revenue']

sales_df = spark.createDataFrame(data=sales_data, schema=sales_schema)
print("Sales DataFrame:")
sales_df.display()

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