A bronze events table stores customer details in a nested column, declared as follows: ```sql customer STRUCT<name: STRING, address: STRUCT<city: STRING, country: STRING>> ``` The silver table must expose the customer's city as a top-level column. The engineer wants the simplest native way to read that nested field, without flattening or custom code. Which approach is correct?

1. A. Dot notation — select customer.address.city, which resolves nested struct fields natively in SQL and PySpark; arrays use bracket indexing like items[0] ✓ A
2. B. Explode the structs first — a nested field can only be read after the entire struct has been flattened into separate rows of the DataFrame 💬 B
3. C. Rewrite the transformation in SQL — PySpark DataFrames cannot traverse fields that live inside a struct column 💬 C
4. D. Register a Python UDF — native functions cannot reach inside a struct, so the field must be parsed out in custom code 💬 D

Reveal answer

Correct answer: Dot notation — select customer.address.city, which resolves nested struct fields natively in SQL and PySpark; arrays use bracket indexing like items[0]

Reference: https://docs.databricks.com/aws/en/pyspark/reference