COF-C03 Exam Dumps - SnowPro Core Certification 2026 Exam

The correct answer is C. GLOBALORGADMIN .

Corrected typos:

The original options contain typing errors:

A. ACCOUNT AUXIN should be ACCOUNTADMIN

B. SYSAEMIN should be SYSADMIN

C. CLOBALORGADMIN should be GLOBALORGADMIN

D. HECURITYADMIH should be SECURITYADMIN

Auto-fulfillment is an organization-level capability related to Snowflake Native Apps and listings across regions or accounts. Because refresh frequency for auto-fulfilled application packages is managed at the organization/global level, the required role is GLOBALORGADMIN.

Why C is correct:

GLOBALORGADMIN is the organization-level administrative role used for managing organization-wide features. Changing the refresh frequency for an auto-fulfilled application package requires this higher-level administrative authority.

Why the other options are incorrect:

A. ACCOUNTADMIN is the highest administrative role within a Snowflake account, but auto-fulfillment refresh frequency is an organization-level setting.

B. SYSADMIN manages account objects such as warehouses, databases, schemas, and tables, but it does not manage organization-level auto-fulfillment settings by default.

D. SECURITYADMIN manages users, roles, and grants, but it is not the default role for managing auto-fulfillment refresh frequency.

Official Snowflake documentation reference:

Snowflake documentation for auto-fulfillment and organization-level administration identifies GLOBALORGADMIN as the role used for managing organization-level Snowflake features.

The minimum Snowflake edition that supports database replication is the Enterprise edition. Database replication allows data to be replicated between different Snowflake accounts or regions, providing high availability and disaster recovery capabilities.

Snowflake Documentation: Database Replication

The correct answer is A. Use a public listing .

A public listing on Snowflake Marketplace allows a provider to make a data product discoverable and available to Snowflake consumers. If the goal is to make a dataset available broadly to Snowflake accounts in the same supported region, a public listing is the recommended sharing method.

Why A is correct:

Public listings are designed for broad distribution of datasets, applications, or services to Snowflake consumers. They are more scalable and discoverable than direct shares when the target audience is many or any Snowflake accounts.

Why the other options are incorrect:

B. A direct share is used to share data with specific known consumer accounts, not broadly with any account in a region.

C. A Data Exchange is a private data hub for a controlled group of members, not the best method for making data broadly available to any Snowflake account.

D. An external stage is used to reference files in cloud storage. It is not Snowflake’s recommended secure sharing mechanism for making datasets available to Snowflake accounts.

Official Snowflake documentation reference:

Snowflake documentation describes listings as the mechanism for offering data products to consumers through Snowflake Marketplace, while direct shares are for sharing with specific accounts.

In a scenario where a Snowflake user queries only a small number of rows that require significant processing and the data in the table does not change frequently, the most effective way to optimize performance is by creating a materialized view based on the query. Materialized views store the result of the query and can significantly reduce the computation time for queries that are executed frequently over unchanged data.

Why Materialized Views:Materialized views precompute and store the result of the query. This is especially beneficial for queries that require heavy processing. Since the data does not change frequently, the materialized view will not need to be refreshed often, making it an ideal solution for this use case.

Implementation Steps:

To create a materialized view, use the following SQL command:

CREATEMATERIALIZEDVIEWmy_materialized_viewASSELECT...FROMmy_tableWHERE...;

When the query is run, Snowflake uses the precomputed results from the materialized view, thus skipping the need for recalculating the data and improving query performance.

In Auto-scale mode, a multi-cluster virtual warehouse can automatically scale out by starting additional clusters as needed to handle concurrent queries . This is ideal for unpredictable or bursty workloads.

Clusters spin up based on queue length and system load .

They automatically shut down when not in use .

When using theCOPY INTO < table > command to load CSV data from a stage into a Snowflake table, one of the crucial guidelines to follow is that the number of columns in each row within the CSV file should be consistent. This ensures data integrity and allows for a smooth data loading process, as Snowflake expects each row in the CSV file to map directly to a row in the target table based on the number of columns. If there ' s a mismatch in the number of columns between any row in the file and the target table, Snowflake might return an error or produce unexpected results during the load operation.

It ' s important to note that while the CSV field delimiter can be specified to something other than a comma, ensuring the consistency in the number of columns across all rows is fundamental to successfully loading data.

The correct answer is B. DROP PROCEDURE SALES_REPORT(INT, INT); .

In Snowflake, stored procedures can be overloaded. This means multiple procedures can have the same name but different argument signatures. Because of this, when dropping a stored procedure, the procedure name and argument data types are used to identify the exact procedure to remove.

Why B is correct:

The procedure was created with two arguments:

MONTH INT, YEAR INT

When dropping the procedure, Snowflake uses the argument data types, not the argument names. Therefore, the correct statement is:

DROP PROCEDURE SALES_REPORT(INT, INT);

Why the other options are incorrect:

A. The argument names are not included in the drop signature. Snowflake expects the argument data types.

C. This includes only argument names without data types, which is not the correct procedure signature syntax.

D. This does not identify the procedure signature and is not sufficient when dropping a stored procedure.

Official Snowflake documentation reference:

Snowflake documentation for DROP PROCEDURE explains that the procedure name and argument data types are required to identify the stored procedure to drop.

The Query Profile tool provides a visual breakdown of query execution including:

Execution time per step

Rows processed , scanned, or returned This helps analyze performance bottlenecks and optimize SQL.