Answer: `raw_df.createOrReplaceTempView("raw_df")`

## Explanation The correct answer is **A** because: - `createOrReplaceTempView("raw_df")` creates a temporary view that is only available for the duration of the current Spark session, which perfectly matches the requirement to "briefly make this data available in SQL for only the remainder of the Spark session" - Temporary views are automatically dropped when the Spark session ends - This allows data analysts to query the data using SQL syntax like `SELECT * FROM raw_df` **Why the other options are incorrect:** - **B** `createTable("raw_df")`: This method doesn't exist in PySpark DataFrame API - **C** `write.save("raw_df")`: This saves the DataFrame to a file system location but doesn't make it available as a SQL table/view - **D** `saveAsTable("raw_df")`: This creates a permanent table in the Hive metastore that persists beyond the current Spark session, which contradicts the requirement for temporary availability The key distinction is that temporary views (`createOrReplaceTempView`) are session-scoped while tables created with `saveAsTable` are persistent and survive session restarts.

Author: LeetQuiz .