While Snowflake primarily deals with structured and semi-structured data, it also has the capability to handle unstructured data. Unstructured data can be stored in Snowflake using variants of SQL data types in tables, which can be permanent tables. These permanent tables, while traditionally used for structured or semi-structured data (like JSON, Avro, or Parquet), can also accommodate unstructured data in the form of binary formats or strings, offering flexibility in data storage and analysis. However, the management and querying of unstructured data in Snowflake may require additional considerations compared to structured data.References: Snowflake Documentation on Data Types

EMP_ID NUMBER,
EMP_NAME VARCHAR(30),
EMP_SALARY NUMBER, DEPT VARCHAR{20)
);
Explanation:
A virtual warehouse in Snowflake is used to perform data processing tasks that require computational resources, such as queries that modify data or perform significant computation. Of the options provided:
C . INSERT INTO TBL_EMPLOYEE(EMP_ID, EMP_NAME, EMP_SALARY, DEPT) VALUES(1,'Adam',20000,'Finance'); This SQL statement performs a data modification operation (DML) by inserting a new record into the TBL_EMPLOYEE table, which requires computational resources provided by a virtual warehouse to execute.
Reference:
Snowflake Documentation: Understanding Virtual Warehouses