Answer: spark.table("sales"), spark.delta.table("sales")

## Explanation The correct answer is **E. spark.table("sales")**. ### Why Option E is correct: - `spark.table("sales")` is the standard PySpark method to access tables registered in the Spark catalog. - This method returns a DataFrame that can be used for data processing, transformations, and testing in PySpark. - It works with Delta tables as well as other table types registered in the Spark catalog. ### Why Option D is also valid: - **D. spark.delta.table("sales")** is a valid alternative specifically for Delta tables. - This method is provided by the Delta Lake library and is optimized for Delta table operations. - However, `spark.table("sales")` is more commonly used and works for all table types. ### Why other options are incorrect: - **A. SELECT * FROM sales**: This is SQL syntax, not PySpark code. While you could use `spark.sql("SELECT * FROM sales")`, the option as written is not valid PySpark code. - **B. There is no way to share data between PySpark and SQL**: This is incorrect. PySpark and SQL can share data through the Spark catalog where tables are registered. - **C. spark.sql("sales")**: This is not a valid SQL query. `spark.sql()` expects a complete SQL statement, not just a table name. ### Key Points: 1. **PySpark-SQL Integration**: PySpark and SQL share the same Spark catalog, allowing seamless data access between both environments. 2. **Delta Table Access**: Delta tables registered in the catalog can be accessed using standard PySpark methods. 3. **Best Practice**: `spark.table("table_name")` is the most common and recommended way to access registered tables in PySpark.

Author: Keng Suppaseth