The GET command is used to download (i.e., unload) files from a Snowflake internal stage to the local file system . COPY INTO writes to stages, not directly to local disk.
Reference:
Snowflake Docs: GET Command

The correct answer is C. To track how data has been transformed among columns .
Snowflake's data lineage capability is used to understand how data flows and changes across objects. It helps users see relationships between source and target objects and understand how data is transformed, especially at the column level.
Corrected wording:
The original option said "records," but Snowflake data lineage is more accurately described as tracking how data is transformed among columns, not individual row-level record transformations.
Why C is correct:
Data lineage helps track how data moves from source columns to target columns through operations such as views, CTAS statements, INSERT statements, and other transformations. This is useful for governance, impact analysis, auditing, and understanding downstream dependencies.
Why the other options are incorrect:
A). Tracking how records are read by a user is more closely related to access history or query history, not data lineage.
B). Tracking dependencies at the object level is related to object dependencies. Data lineage is more specific because it can show how data flows between columns and objects.
D). Snowflake data lineage is not primarily a row-level feature for tracking the impact of transformations on individual records.
Official Snowflake documentation reference:
Snowflake documentation describes data lineage as a governance capability that helps track how data moves and transforms across Snowflake objects, including column-level relationships.
Reference: Snowflake Documentation - Data lineage; Snowflake Documentation - Object dependencies; SnowPro Core Study Guide - Data Protection and Governance.
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ThePARTITION BY < expression > parameter option in theCOPY INTO < location > command is used to split the output into multiple files based on the distinct values of the specified expression. This feature is particularly useful for organizing large datasets into smaller, more manageable files and can help with optimizing downstream processing or consumption of the data. For example, if you are unloading a large dataset of transactions and usePARTITION BY DATE(transactions.transaction_date), Snowflake generates a separate output file for each unique transaction date, facilitating easier data management and access.
This approach to data unloading can significantly improve efficiency when dealing with large volumes of data by enabling parallel processing and simplifying data retrieval based on specific criteria or dimensions.
References:
Snowflake Documentation on Unloading Data: COPY INTO < location >