COF-C02 Exam Dumps - SnowPro Core Certification Exam

A multi-cluster warehouse should be used in auto-scaling mode when there is a need to handle a large number of concurrent queries. Auto-scaling allows Snowflake to automatically add or remove compute clusters to balance the load, ensuring that performance remains consistent during varying levels of demand

 Snowflake’s Fail-safe provides a 7-day period during which data in a permanent table may be recoverable, but only by Snowflake Support, not by user operations3.

In Snowflake, the query history and the results of queries executed by a user are accessible based on the roles and permissions. If User1 executed a query with the SYSADMIN role, User2 would be able to view the result set of that query executed by User1 only if User2 has the ACCOUNTADMIN role. The ACCOUNTADMIN role has the broadest set of privileges, including the ability to access all aspects of the account's operation, data, and query history, thus enabling User2 to view the results of queries executed by other users.

References:

Snowflake Documentation: Understanding Snowflake Roles

The worksheet and database explorer feature in Snowsight allows users to tag frequently accessed worksheets for ease of access. This functionality helps users organize and quickly navigate to the worksheets they use most often, enhancing productivity and streamlining the data exploration and analysis process within Snowsight, Snowflake's web-based query and visualization interface.

References:

Snowflake Documentation: Snowsight (UI for Snowflake)

Transient tables in Snowflake are designed for temporary or intermediate workloads with the following characteristics:

B. Transient tables can be cloned to permanent tables: This feature allows users to create copies of transient tables for permanent use, providing flexibility in managing data lifecycles.

C. Transient tables persist until they are explicitly dropped: Unlike temporary tables that exist for the duration of a session, transient tables remain in the database until explicitly removed by a user, offering more durability for short-term data storage needs.

References:

Snowflake Documentation: Transient Tables

The Query Profile in Snowflake can identify common query problems, including:

B. Inefficient pruning: This refers to the inability of a query to effectively limit the amount of data being scanned, potentially leading to suboptimal performance.

D. Queries too large to fit in memory: This indicates that a query requires more memory than is available in the virtual warehouse, which can lead to spilling to disk and degraded performance.

The Query Profile helps diagnose these issues by providing detailed execution statistics and visualizations, aiding in query optimization and troubleshooting.

References:

Snowflake Documentation: Query Profile

Top of Form

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

References:

Snowflake Documentation: Using the Parquet File Format for Unloading Data

To get the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view should be used. This Information Schema view provides granular storage metrics for tables within Snowflake, including data related to the size of the table, the amount of data stored, and storage usage over time. It's an essential tool for administrators and users looking to monitor and optimize storage consumption and costs.

References:

Snowflake Documentation: Information Schema - TABLE STORAGE METRICS View