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A is True. It's evident. Explanations for B: A Pandas dataframe is a relational object where the data is represented as rows and columns, think of it as an Excel spreadsheet. All data in a Pandas DataFrame is stored in client memory, and all operations and transformations on the DataFrame object are performed instantly. A Snowpark DataFrame is also a relational object, but the data is stored in Snowflake. The Snowpark DataFrame object contains only the logic, SQL, needed to retrieve data according to the operations and transformations applied to the DataFrame object. It is also evaluated lazily, meaning that it only executes the logic, the SQL, when a specific action is triggered. So B is also True
Referring to Columns in Different DataFrames When referring to columns in two different DataFrame objects that have the same name (for example, joining the DataFrames on that column), you can use the DataFrame.col method in one DataFrame object to refer to a column in that object (for example, df1.col("name") and df2.col("name")). To retrieve and manipulate data, you use the DataFrame class. A DataFrame represents a relational dataset that is evaluated lazily: it only executes when a specific action is triggered. In a sense, a DataFrame is like a query that needs to be evaluated in order to retrieve data. https://docs.snowflake.com/en/developer-guide/snowpark/python/working-with-dataframes