Answer: spark.table("sales")

## Explanation In PySpark, there are multiple ways to access Delta tables: 1. **spark.table("sales")** - This is the correct method to access a registered table in PySpark. When a Delta table is created, it's typically registered in the Spark catalog, and `spark.table()` can be used to access it. 2. **spark.sql("SELECT * FROM sales")** - While option A shows `SELECT * FROM sales` alone, in PySpark you would need to wrap it in `spark.sql()` to execute SQL queries. 3. **spark.read.table("sales")** - Another valid method for reading tables. 4. **spark.read.format("delta").table("sales")** - For explicitly specifying Delta format. **Why other options are incorrect:** - **A**: `SELECT * FROM sales` alone is not valid PySpark syntax - it needs to be wrapped in `spark.sql()` - **B**: This is false - Delta tables can be accessed from both SQL and PySpark - **C**: `spark.sql("sales")` is incorrect syntax - `spark.sql()` expects a SQL query, not just a table name - **D**: `spark.delta.table("sales")` is not valid PySpark syntax - there's no `spark.delta.table()` method The most straightforward and correct way to access a registered Delta table in PySpark is `spark.table("sales")`.

Author: Keng Suppaseth