In a scenario where you are working with Azure Databricks and need to process a large dataset stored in a CSV file named 'sales_data.csv' located in the 'data' directory of your Azure Blob Storage. The dataset contains sales transactions with a header row and you want to ensure that the schema is automatically inferred to avoid manual schema definition. Additionally, you need to create a table named 'sales_table' for further analysis. Considering the requirements of cost efficiency, performance, and schema inference, which of the following Spark DataFrame queries would you use to read the CSV file and create the table? Choose the best option and explain why it is the most suitable for this scenario.