In which scenario will use of an external table simplify a data pipeline?

A. Accessing a Snowflake table from a relational database requires a connector (e.g., JDBC/ODBC), not an external table, which reads data into Snowflake.
B. This scenario is similar to option A; it describes accessing Snowflake from an external system, which is the reverse of an external table's function.
C. Writing data from Snowflake to external storage is accomplished using the COPY INTO command (data unloading), not by using an external table, which is read-only.

1. Snowflake Documentation: Overview of External Tables.

Section: Introduction.

Content: "External tables let you query data stored in files in an external stage as if the data were inside a table in Snowflake... Querying data in an external table may be slower than querying data that has been loaded into Snowflake. However

external tables are useful for querying large data sets that would be too time-consuming and expensive to load into Snowflake." This directly supports the concept of accessing data files in cloud storage.

2. Snowflake Documentation: Data Pipelines.

Section: Continuous Data Loading.

Content: The documentation contrasts Snowpipe for continuous loading with external tables for direct querying. It positions external tables as a way to "query files in a data lake without loading them

" which simplifies the pipeline by removing the loading step.

3. Snowflake Documentation: CREATE EXTERNAL TABLE.

Section: Usage Notes.

Content: "External tables are read-only. You cannot perform any DML operations on them." This explicitly refutes option C

which involves writing data. The entire command syntax is built around referencing a LOCATION (an external stage)

which validates option D.