COF-C02 Exam Dumps - SnowPro Core Certification Exam

 For loading data into Snowflake via Snowpipe, the recommended compressed file size is between 100-250 MB. This size range is optimal for balancing the performance of parallel processing and minimizing the overhead associated with handling many small files2.

The command USE WAREHOUSE <>; is used to set the virtual warehouse for the current session in Snowflake. This command specifies which virtual warehouse to use for executing queries in that session1.

 In Snowflake, a task allows a user to execute a single SQL statement/command using a predefined schedule (B). Tasks are used to automate the execution of SQL statements at scheduled intervals.

 The Snowflake Cloud Services layer manages various services, including authentication and metadata management. This layer ties together all the different components of Snowflake to process user requests, manage sessions, and control access3.

In Snowflake, to extract a specific element from a JSON stored in a VARIANT column, the correct syntax is to use the dot notation. Therefore, the query select src.dealership from car_sales; will return the dealership information contained within each JSON object in the src column.

References: For a detailed explanation, please refer to the Snowflake documentation on querying semi-structured data.

The Snowflake cloud services layer is responsible for tasks such as authentication and access control, ensuring secure access to the platform, and metadata management, which involves organizing and maintaining information about the data stored in Snowflake56.

When loading data into Snowflake, it is recommended to organize the data into single files with 100-250 MB of compressed data per file. This size range is optimal for parallel processing and can help in achieving better performance during data loading operations. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The ResultSet cache is leveraged to quickly return results for repeated queries. Actions that prevent leveraging this cache include stopping the virtual warehouse that the query is running against (B) and executing the RESULTS_SCAN() table function (D). Stopping the warehouse clears the local disk cache, including the ResultSet cache1. The RESULTS_SCAN() function is used to retrieve the result of a previously executed query, which bypasses the need for the ResultSet cache.