Snowflake COF-C02 Practice Exams
Last updated on Oct 22,2026- Exam Code: COF-C02
- Exam Name: SnowPro Core Certification Exam
- Certification Provider: Snowflake
- Latest update: Oct 22,2026
Question #71
How do Snowflake data providers share data that resides in different databases?
- A . External tables
- B . Secure views
- C . Materialized views
- D . User-Defined Functions (UDFs)
Correct Answer: B
B
Explanation:
Snowflake data providers can share data residing in different databases through secure views. Secure views allow for the referencing of objects such as schemas, tables, and other views contained in one or more databases, as long as those databases belong to the same account. This enables providers to share data securely and efficiently with consumers.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
B
Explanation:
Snowflake data providers can share data residing in different databases through secure views. Secure views allow for the referencing of objects such as schemas, tables, and other views contained in one or more databases, as long as those databases belong to the same account. This enables providers to share data securely and efficiently with consumers.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question #72
Which columns are part of the result set of the Snowflake LATERAL FLATTEN command? (Choose
two.)
- A . CONTENT
- B . PATH
- C . BYTE_SIZE
- D . INDEX
- E . DATATYPE
Correct Answer: B, D
B, D
Explanation:
The LATERAL FLATTEN command in Snowflake produces a result set that includes several columns, among which PATH and INDEX are includedPATH indicates the path to the element within a data structure that needs to be flattened, and INDEX represents the index of the element if it is an array2.
B, D
Explanation:
The LATERAL FLATTEN command in Snowflake produces a result set that includes several columns, among which PATH and INDEX are includedPATH indicates the path to the element within a data structure that needs to be flattened, and INDEX represents the index of the element if it is an array2.
Question #73
What does Snowflake recommend when planning virtual warehouse usage for a data load?
- A . Load the fewest possible number of large files.
- B . Dedicate a separate warehouse for loading data.
- C . Increase the size of the warehouse used.
- D . Use several single-duster warehouses.
Correct Answer: B
Question #74
What actions can be performed by a consumer account on a shared database? (Select TWO)
- A . Cloning a shared table
- B . Modifying the data in a shared table
- C . Using Time Travel on a shared table
- D . Executing the select statement on a shared table
- E . Joining the data from a shared table with another table
Correct Answer: D, E
Question #75
Which type of charts are supported by Snowsight? {Select TWO)
- A . Flowcharts
- B . Gantt charts
- C . Line charts
- D . Pie charts
- E . Scatterplots
Correct Answer: D, E
Question #76
What are valid sub-clauses to the OVER clause for a window function? (Select TWO).
- A . GROUP BY
- B . LIMIT
- C . ORDER BY
- D . PARTITION BY
- E . UNION ALL
Correct Answer: C, D
C, D
Explanation:
Valid sub-clauses to the OVER clause for a window function in SQL are:
C. ORDER BY: This clause specifies the order in which the rows in a partition are processed by the window function. It is essential for functions that depend on the row order, such as ranking functions.
D. PARTITION BY: This clause divides the result set into partitions to which the window function is applied. Each partition is processed independently of other partitions, making it crucial for functions that compute values across sets of rows that share common characteristics.
These clauses are fundamental to defining the scope and order of data over which the window function operates, enabling complex analytical computations within SQL queries.
References:
Snowflake Documentation: Window Functions
C, D
Explanation:
Valid sub-clauses to the OVER clause for a window function in SQL are:
C. ORDER BY: This clause specifies the order in which the rows in a partition are processed by the window function. It is essential for functions that depend on the row order, such as ranking functions.
D. PARTITION BY: This clause divides the result set into partitions to which the window function is applied. Each partition is processed independently of other partitions, making it crucial for functions that compute values across sets of rows that share common characteristics.
These clauses are fundamental to defining the scope and order of data over which the window function operates, enabling complex analytical computations within SQL queries.
References:
Snowflake Documentation: Window Functions
Question #77
Which services does the Snowflake Cloud Services layer manage? (Select TWO).
- A . Compute resources
- B . Query execution
- C . Authentication
- D . Data storage
- E . Metadata
Correct Answer: C, E
C, E
Explanation:
The Snowflake Cloud Services layer manages a variety of services that are crucial for the operation of the Snowflake platform. Among these services, Authentication and Metadata management are key components. Authentication is essential for controlling access to the Snowflake environment, ensuring that only authorized users can perform actions within the platform. Metadata management involves handling all the metadata related to objects within Snowflake, such as tables, views, and databases, which is vital for the organization and retrieval of data.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation12
https://docs.snowflake.com/en/user-guide/intro-key-concepts.html
C, E
Explanation:
The Snowflake Cloud Services layer manages a variety of services that are crucial for the operation of the Snowflake platform. Among these services, Authentication and Metadata management are key components. Authentication is essential for controlling access to the Snowflake environment, ensuring that only authorized users can perform actions within the platform. Metadata management involves handling all the metadata related to objects within Snowflake, such as tables, views, and databases, which is vital for the organization and retrieval of data.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation12
https://docs.snowflake.com/en/user-guide/intro-key-concepts.html
Question #78
What does Snowflake recommend regarding database object ownership? (Select TWO).
- A . Create objects with ACCOUNTADMIN and do not reassign ownership.
- B . Create objects with SYSADMIN.
- C . Create objects with SECURITYADMIN to ease granting of privileges later.
- D . Create objects with a custom role and grant this role to SYSADMIN.
- E . Use only MANAGED ACCESS SCHEMAS for66 objects owned by ACCOUNTADMIN.
Correct Answer: B, D
B, D
Explanation:
Snowflake recommends creating objects with a role that has the necessary privileges and is not overly permissive. SYSADMIN is typically used for managing system-level objects and operations. Creating objects with a custom role and granting this role to SYSADMIN allows for more granular control and adherence to the principle of least privilege.
References: Based on best practices for database object ownership and role management.
B, D
Explanation:
Snowflake recommends creating objects with a role that has the necessary privileges and is not overly permissive. SYSADMIN is typically used for managing system-level objects and operations. Creating objects with a custom role and granting this role to SYSADMIN allows for more granular control and adherence to the principle of least privilege.
References: Based on best practices for database object ownership and role management.
Question #79
How does Snowflake reorganize data when it is loaded? (Select TWO).
- A . Binary format
- B . Columnar format
- C . Compressed format
- D . Raw format
- E . Zipped format
Correct Answer: B, C
B, C
Explanation:
When data is loaded into Snowflake, it undergoes a reorganization process where the data is stored in a columnar format and compressed. The columnar storage format enables efficient querying and data retrieval, as it allows for reading only the necessary columns for a query, thereby reducing IO operations. Additionally, Snowflake uses advanced compression techniques to minimize storage costs and improve performance. This combination of columnar storage and compression is key to Snowflake’s data warehousing capabilities.
References: Snowflake Documentation: Data Storage and Organization
B, C
Explanation:
When data is loaded into Snowflake, it undergoes a reorganization process where the data is stored in a columnar format and compressed. The columnar storage format enables efficient querying and data retrieval, as it allows for reading only the necessary columns for a query, thereby reducing IO operations. Additionally, Snowflake uses advanced compression techniques to minimize storage costs and improve performance. This combination of columnar storage and compression is key to Snowflake’s data warehousing capabilities.
References: Snowflake Documentation: Data Storage and Organization
Question #80
Which of the following statements describe features of Snowflake data caching? (Choose two.)
- A . When a virtual warehouse is suspended, the data cache is saved on the remote storage layer.
- B . When the data cache is full, the least-recently used data will be cleared to make room.
- C . A user can only access their own queries from the query result cache.
- D . A user must set USE_METADATA_CACHE to TRUE to use the metadata cache in queries.
- E . The RESULT_SCAN table function can access and filter the contents of the query result cache.
Correct Answer: B, E
B, E
Explanation:
Snowflake’s data caching features include the ability to clear the least-recently used data when the data cache is full to make room for new data. Additionally, the RESULT_SCAN table function can access and filter the contents of the query result cache, allowing users to retrieve and work with the results of previous queries. The other statements are incorrect: the data cache is not saved on the remote storage layer when a virtual warehouse is suspended, users can access queries from the query result cache that were run by other users, and there is no setting called USE_METADATA_CACHE in Snowflake.
References: Caching in the Snowflake Cloud Data Platform, Optimizing the warehouse cache
B, E
Explanation:
Snowflake’s data caching features include the ability to clear the least-recently used data when the data cache is full to make room for new data. Additionally, the RESULT_SCAN table function can access and filter the contents of the query result cache, allowing users to retrieve and work with the results of previous queries. The other statements are incorrect: the data cache is not saved on the remote storage layer when a virtual warehouse is suspended, users can access queries from the query result cache that were run by other users, and there is no setting called USE_METADATA_CACHE in Snowflake.
References: Caching in the Snowflake Cloud Data Platform, Optimizing the warehouse cache