Snowflake COF-C02 Practice Exams
Last updated on Oct 24,2026- Exam Code: COF-C02
- Exam Name: SnowPro Core Certification Exam
- Certification Provider: Snowflake
- Latest update: Oct 24,2026
Question #241
What kind of value does a User-Defined Function (UDF) return? (Select TWO).
- A . Dictionary
- B . List
- C . Object
- D . Scalar
- E . Tabular
Correct Answer: D, E
Question #242
Using which object-level parameters will help limit query processing and concurrency slowdowns? (Select TWO).
A)
B)
C)
D)
E)
- A . Option A
- B . Option B
- C . Option C
- D . Option D
Correct Answer: C, D
Question #243
Which Snowflake table type is only visible to the user who creates it, can have the same name as permanent tables in the same schema, and is dropped at the end of the session?
- A . Temporary
- B . Local
- C . User
- D . Transient
Correct Answer: A
A
Explanation:
In Snowflake, a Temporary table is a type of table that is only visible to the user who creates it, can have the same name as permanent tables in the same schema, and is automatically dropped at the end of the session in which it was created. Temporary tables are designed for transient data processing needs, where data is needed for the duration of a specific task or session but not beyond. Since they are automatically cleaned up at the end of the session, they help manage storage usage efficiently and ensure that sensitive data is not inadvertently persisted.
References: Snowflake Documentation on Temporary Tables: Temporary Tables
A
Explanation:
In Snowflake, a Temporary table is a type of table that is only visible to the user who creates it, can have the same name as permanent tables in the same schema, and is automatically dropped at the end of the session in which it was created. Temporary tables are designed for transient data processing needs, where data is needed for the duration of a specific task or session but not beyond. Since they are automatically cleaned up at the end of the session, they help manage storage usage efficiently and ensure that sensitive data is not inadvertently persisted.
References: Snowflake Documentation on Temporary Tables: Temporary Tables
Question #244
Which of the following are benefits of micro-partitioning? (Select TWO)
- A . Micro-partitions cannot overlap in their range of values
- B . Micro-partitions are immutable objects that support the use of Time Travel.
- C . Micro-partitions can reduce the amount of I/O from object storage to virtual warehouses
- D . Rows are automatically stored in sorted order within micro-partitions
- E . Micro-partitions can be defined on a schema-by-schema basis
Correct Answer: B, C
B, C
Explanation:
Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake’s query optimizer can skip over micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html
B, C
Explanation:
Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake’s query optimizer can skip over micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html
Question #245
How is unstructured data retrieved from data storage?
- A . SQL functions like the GET command can be used to copy the unstructured data to a location on the client.
- B . SQL functions can be used to create different types of URLs pointing to the unstructured data.
These URLs can be used to download the data to a client. - C . SQL functions can be used to retrieve the data from the query results cache. When the query results are output to a client, the unstructured data will be output to the client as files.
- D . SQL functions can call on different web extensions designed to display different types of files as a web page. The web extensions will allow the files to be downloaded to the client.
Correct Answer: B
B
Explanation:
Unstructured data stored in Snowflake can be retrieved by using SQL functions to generate URLs that point to the data. These URLs can then be used to download the data directly to a client
B
Explanation:
Unstructured data stored in Snowflake can be retrieved by using SQL functions to generate URLs that point to the data. These URLs can then be used to download the data directly to a client
Question #246
Which privilege is required for a user to be able to view a resource monitor?
- A . USAGE
- B . SELECT
- C . MONITOR
- D . OPERATE
Correct Answer: C
Question #247
When unloading to a stage, which of the following is a recommended practice or approach?
- A . Set SINGLE: = true for larger files
- B . Use OBJECT_CONSTRUCT ( * ) when using Parquet
- C . Avoid the use of the CAST function
- D . Define an individual file format
Correct Answer: D
D
Explanation:
When unloading data to a stage, it is recommended to define an individual file format. This ensures that the data is unloaded in a consistent and expected format, which can be crucial for downstream processing and analysis2
D
Explanation:
When unloading data to a stage, it is recommended to define an individual file format. This ensures that the data is unloaded in a consistent and expected format, which can be crucial for downstream processing and analysis2
Question #248
True or False: Loading data into Snowflake requires that source data files be no larger than 16MB.
- A . True
- B . False
Correct Answer: B
B
Explanation:
Snowflake does not require source data files to be no larger than 16MB. In fact, Snowflake recommends that for optimal load performance, data files should be roughly 100-250 MB in size when compressed. However, it is not recommended to load very large files (e.g., 100 GB or larger) due to potential delays and wasted credits if errors occur. Smaller files should be aggregated to minimize processing overhead, and larger files should be split to distribute the load among compute resources in an active warehouse.
References: Preparing your data files | Snowflake Documentation
B
Explanation:
Snowflake does not require source data files to be no larger than 16MB. In fact, Snowflake recommends that for optimal load performance, data files should be roughly 100-250 MB in size when compressed. However, it is not recommended to load very large files (e.g., 100 GB or larger) due to potential delays and wasted credits if errors occur. Smaller files should be aggregated to minimize processing overhead, and larger files should be split to distribute the load among compute resources in an active warehouse.
References: Preparing your data files | Snowflake Documentation
Question #249
What virtual warehouse configuration should be used when processing a large number of complex queries?
- A . Use the auto-resume feature.
- B . Run the warehouse in auto-scale mode.
- C . Increase the size of the warehouse.
- D . Increase the number of warehouse clusters.
Correct Answer: D
D
Explanation:
To handle a large number of complex queries, configuring the warehouse in auto-scale mode by increasing the number of warehouse clusters is recommended. This setup allows Snowflake to dynamically add clusters as demand increases, ensuring better performance and concurrency. Increasing the number of clusters provides scalability for concurrent users and heavy workloads, improving response times without impacting individual query performance.
D
Explanation:
To handle a large number of complex queries, configuring the warehouse in auto-scale mode by increasing the number of warehouse clusters is recommended. This setup allows Snowflake to dynamically add clusters as demand increases, ensuring better performance and concurrency. Increasing the number of clusters provides scalability for concurrent users and heavy workloads, improving response times without impacting individual query performance.
Question #250
Which cache type is used to cache data output from SQL queries?
- A . Metadata cache
- B . Result cache
- C . Remote cache
- D . Local file cache
Correct Answer: B
B
Explanation:
The Result cache is used in Snowflake to cache the data output from SQL queries. This feature is designed to improve performance by storing the results of queries for a period of time. When the same or similar query is executed again, Snowflake can retrieve the result from this cache instead of re-computing the result, which saves time and computational resources.
References:
Snowflake Documentation on Query Results Cache
SnowPro® Core Certification Study Guide
B
Explanation:
The Result cache is used in Snowflake to cache the data output from SQL queries. This feature is designed to improve performance by storing the results of queries for a period of time. When the same or similar query is executed again, Snowflake can retrieve the result from this cache instead of re-computing the result, which saves time and computational resources.
References:
Snowflake Documentation on Query Results Cache
SnowPro® Core Certification Study Guide