ARA-C01 Data Engineering Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Set the CATALOG to 'AWS_GLUE' to ensure that other AWS services can manage the metadata.

  Why it's wrong here

  Using an external catalog like Glue makes Snowflake a 'read-only' consumer of the Iceberg table's metadata. This limits Snowflake's ability to optimize the table and perform high-performance DML, which contradicts the goal of maximizing Snowflake's performance while using an open storage format in S3.

• ✓

  Set the CATALOG to 'SNOWFLAKE' and specify an EXTERNAL_VOLUME.

  Why this is correct

  When the catalog is set to Snowflake, the platform handles all metadata management, allowing for performance optimizations similar to native tables. The External Volume defines the connection to the customer's S3 bucket, ensuring the data remains in their account in the open Parquet-based Iceberg format.

• ✗

  Use the 'EXTERNAL' catalog type and point to a Snowflake Managed Iceberg Catalog.

  Why it's wrong here

  The 'EXTERNAL' catalog type is used when a service other than Snowflake (like Polaris or Glue) is the source of truth for the table's metadata. This configuration is used for cross-engine interoperability but generally offers less performance optimization within Snowflake compared to using the Snowflake-managed catalog.

• ✗

  Create a standard Snowflake table and use a periodic Task to export the data to S3 in Iceberg format.

  Why it's wrong here

  Exporting data manually via tasks creates a redundant copy and does not constitute a true Lakehouse architecture. Iceberg tables are designed to be the primary storage format, allowing Snowflake to query the data in-place without the need for complex and costly export/synchronization pipelines.