Snowflake COF-C02 Practice Exams
Last updated on Oct 21,2026- Exam Code: COF-C02
- Exam Name: SnowPro Core Certification Exam
- Certification Provider: Snowflake
- Latest update: Oct 21,2026
Question #1
User INQUISITIVE_PERSON has been granted the role DATA_SCIENCE. The role DATA_SCIENCE has privileges OWNERSHIP on the schema MARKETING of the database ANALYTICS_DW.
Which command will show all privileges granted to that schema?
- A . SHOW GRANTS ON ROLE DATA_SCIENCE
- B . SHOW GRANTS ON SCHEMA ANALYTICS_DW.MARKETING
- C . SHOW GRANTS TO USER INQUISITIVE_PERSON
- D . SHOW GRANTS OF ROLE DATA_SCIENCE
Correct Answer: B
B
Explanation:
To show all privileges granted to a specific schema, the command SHOW GRANTS ON SCHEMA <schema_name> should be used3. In this case, it would be SHOW GRANTS ON SCHEMA ANALYTICS_DW.MARKETING.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
B
Explanation:
To show all privileges granted to a specific schema, the command SHOW GRANTS ON SCHEMA <schema_name> should be used3. In this case, it would be SHOW GRANTS ON SCHEMA ANALYTICS_DW.MARKETING.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question #2
Which table type is used in the file processing pipeline to process unstructured data in Snowflake?
- A . Temporary
- B . Directory
- C . Standard
- D . Transient
Correct Answer: B
Question #3
When loading data into Snowflake, how should the data be organized?
- A . Into single files with 100-250 MB of compressed data per file
- B . Into single files with 1-100 MB of compressed data per file
- C . Into files of maximum size of 1 GB of compressed data per file
- D . Into files of maximum size of 4 GB of compressed data per file
Correct Answer: A
A
Explanation:
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
A
Explanation:
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
Question #4
How should a Snowflake use’ configure a virtual warehouse to be in Maximized mode”
- A . Set the WAREHOUSES_SIZE to 6XL.
- B . Set the STATEMENT_TIMEOUT_1M_SECOMES to 0.
- C . Set the MAX_CONCURRENCY_LEVEL to a value of 12 or large.
- D . Set the same value for both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT.
Correct Answer: D
D
Explanation:
In Snowflake, configuring a virtual warehouse to be in a "Maximized" mode implies maximizing the resources allocated to the warehouse for its duration. This is done to ensure that the warehouse has a consistent amount of compute resources available, enhancing performance for workloads that require a high level of parallel processing or for handling high query volumes.
To configure a virtual warehouse in maximized mode, you should set the same value for both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT. This configuration ensures that the warehouse operates with a fixed number of clusters, thereby providing a stable and maximized level of compute resources.
Reference to Snowflake documentation on warehouse sizing and scaling:
Warehouse Sizing and Scaling
Understanding Warehouses
D
Explanation:
In Snowflake, configuring a virtual warehouse to be in a "Maximized" mode implies maximizing the resources allocated to the warehouse for its duration. This is done to ensure that the warehouse has a consistent amount of compute resources available, enhancing performance for workloads that require a high level of parallel processing or for handling high query volumes.
To configure a virtual warehouse in maximized mode, you should set the same value for both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT. This configuration ensures that the warehouse operates with a fixed number of clusters, thereby providing a stable and maximized level of compute resources.
Reference to Snowflake documentation on warehouse sizing and scaling:
Warehouse Sizing and Scaling
Understanding Warehouses
Question #5
Which command is used to upload data files from a local directory or folder on a client machine to an internal stage, for a specified table?
- A . GET
- B . PUT
- C . CREATE STREAM
- D . COPY INTO <location>
Correct Answer: B
B
Explanation:
To upload data files from a local directory or folder on a client machine to an internal stage in Snowflake, the PUT command is used. The PUT command takes files from the local file system and uploads them to an internal Snowflake stage (or a specified stage) for the purpose of preparing the data to be loaded into Snowflake tables.
Syntax Example:
PUT file://<local_file_path> @<stage_name>;
This command is crucial for data ingestion workflows in Snowflake, especially when preparing to load data using the COPY INTO command.
Reference: Snowflake Documentation on Using the PUT Command:
https://docs.snowflake.com/en/sql-reference/sql/put.html
B
Explanation:
To upload data files from a local directory or folder on a client machine to an internal stage in Snowflake, the PUT command is used. The PUT command takes files from the local file system and uploads them to an internal Snowflake stage (or a specified stage) for the purpose of preparing the data to be loaded into Snowflake tables.
Syntax Example:
PUT file://<local_file_path> @<stage_name>;
This command is crucial for data ingestion workflows in Snowflake, especially when preparing to load data using the COPY INTO command.
Reference: Snowflake Documentation on Using the PUT Command:
https://docs.snowflake.com/en/sql-reference/sql/put.html
Question #6
Which Snowflake data governance feature can support auditing when a user query reads column data?
- A . Access History
- B . Data classification
- C . Column-level security
- D . Object dependencies
Correct Answer: A
A
Explanation:
Access History in Snowflake is a feature designed to support auditing by tracking access to data within Snowflake, including when a user’s query reads column data. It provides detailed information on queries executed, including the user who ran the query, the query text, and the objects (e.g., tables, views) accessed by the query. This feature is instrumental for auditing purposes, helping organizations to monitor and audit data access for security and compliance.
Reference: Snowflake Documentation on Access History which details its use for auditing access to data: https://docs.snowflake.com/en/user-guide/access-history.html
A
Explanation:
Access History in Snowflake is a feature designed to support auditing by tracking access to data within Snowflake, including when a user’s query reads column data. It provides detailed information on queries executed, including the user who ran the query, the query text, and the objects (e.g., tables, views) accessed by the query. This feature is instrumental for auditing purposes, helping organizations to monitor and audit data access for security and compliance.
Reference: Snowflake Documentation on Access History which details its use for auditing access to data: https://docs.snowflake.com/en/user-guide/access-history.html
Question #7
A user wants to access files stored in a stage without authenticating into Snowflake.
Which type of URL should be used?
- A . File URL
- B . Staged URL
- C . Scoped URL
- D . Pre-signed URL
Correct Answer: D
D
Explanation:
A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
D
Explanation:
A Pre-signed URL should be used to access files stored in a Snowflake stage without requiring authentication into Snowflake. Pre-signed URLs are simple HTTPS URLs that provide temporary access to a file via a web browser, using a pre-signed access token. The expiration time for the access token is configurable, and this type of URL allows users or applications to directly access or download the files without needing to authenticate into Snowflake5.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question #8
How does Snowflake allow a data provider with an Azure account in central Canada to share data with a data consumer on AWS in Australia?
- A . The data provider in Azure Central Canada can create a direct share to AWS Asia Pacific, if they are both in the same organization.
- B . The data consumer and data provider can form a Data Exchange within the same organization to create a share from Azure Central Canada to AWS Asia Pacific.
- C . The data provider uses the GET DATA workflow in the Snowflake Data Marketplace to create a share between Azure Central Canada and AWS Asia Pacific.
- D . The data provider must replicate the database to a secondary account in AWS Asia Pacific within the same organization then create a share to the data consumer’s account.
Correct Answer: D
D
Explanation:
Snowflake allows data providers to share data with consumers across different cloud platforms and regions through database replication. The data provider must replicate the database to a secondary account in the target region or cloud platform within the same organization, and then create a share to the data consumer’s account. This process ensures that the data is available in the consumer’s region and on their cloud platform, facilitating seamless data sharing.
References: Sharing data securely across regions and cloud platforms | Snowflake Documentation
D
Explanation:
Snowflake allows data providers to share data with consumers across different cloud platforms and regions through database replication. The data provider must replicate the database to a secondary account in the target region or cloud platform within the same organization, and then create a share to the data consumer’s account. This process ensures that the data is available in the consumer’s region and on their cloud platform, facilitating seamless data sharing.
References: Sharing data securely across regions and cloud platforms | Snowflake Documentation
Question #9
Which type of workload is recommended for Snowpark-optimized virtual warehouses?
- A . Workloads with ad hoc analytics
- B . Workloads that have large memory requirements
- C . Workloads with unpredictable data volumes for each query
- D . Workloads that are queried with small table scans and selective filters
Correct Answer: B
B
Explanation:
Snowpark-optimized virtual warehouses in Snowflake are designed to efficiently handle workloads with large memory requirements. Snowpark is a developer framework that allows users to write code in languages like Scala, Java, and Python to process data in Snowflake. Given the nature of these programming languages and the types of data processing tasks they are typically used for, having a virtual warehouse that can efficiently manage large memory-intensive operations is crucial.
Understanding Snowpark-Optimized Virtual Warehouses:
Snowpark allows developers to build complex data pipelines and applications within Snowflake using familiar programming languages.
These virtual warehouses are optimized to handle the execution of Snowpark workloads, which often
involve large datasets and memory-intensive operations.
Large Memory Requirements:
Workloads with large memory requirements include data transformations, machine learning model training, and advanced analytics.
These operations often need to process significant amounts of data in memory to perform efficiently.
Snowpark-optimized virtual warehouses are configured to provide the necessary memory resources to support these tasks, ensuring optimal performance and scalability.
Other Considerations:
While Snowpark can handle other types of workloads, its optimization for large memory tasks makes it particularly suitable for scenarios where data processing needs to be done in-memory.
Snowflake’s ability to scale compute resources dynamically also plays a role in efficiently managing large memory workloads, ensuring that performance is maintained even as data volumes grow.
References:
Snowflake Documentation: Introduction to Snowpark
Snowflake Documentation: Virtual Warehouses
B
Explanation:
Snowpark-optimized virtual warehouses in Snowflake are designed to efficiently handle workloads with large memory requirements. Snowpark is a developer framework that allows users to write code in languages like Scala, Java, and Python to process data in Snowflake. Given the nature of these programming languages and the types of data processing tasks they are typically used for, having a virtual warehouse that can efficiently manage large memory-intensive operations is crucial.
Understanding Snowpark-Optimized Virtual Warehouses:
Snowpark allows developers to build complex data pipelines and applications within Snowflake using familiar programming languages.
These virtual warehouses are optimized to handle the execution of Snowpark workloads, which often
involve large datasets and memory-intensive operations.
Large Memory Requirements:
Workloads with large memory requirements include data transformations, machine learning model training, and advanced analytics.
These operations often need to process significant amounts of data in memory to perform efficiently.
Snowpark-optimized virtual warehouses are configured to provide the necessary memory resources to support these tasks, ensuring optimal performance and scalability.
Other Considerations:
While Snowpark can handle other types of workloads, its optimization for large memory tasks makes it particularly suitable for scenarios where data processing needs to be done in-memory.
Snowflake’s ability to scale compute resources dynamically also plays a role in efficiently managing large memory workloads, ensuring that performance is maintained even as data volumes grow.
References:
Snowflake Documentation: Introduction to Snowpark
Snowflake Documentation: Virtual Warehouses
Question #10
A marketing co-worker has requested the ability to change a warehouse size on their medium virtual warehouse called mktg__WH.
Which of the following statements will accommodate this request?
- A . ALLOW RESIZE ON WAREHOUSE MKTG__WH TO USER MKTG__LEAD;
- B . GRANT MODIFY ON WAREHOUSE MKTG WH TO ROLE MARKETING;
- C . GRANT MODIFY ON WAREHOUSE MKTG__WH TO USER MKTG__LEAD;
- D . GRANT OPERATE ON WAREHOUSE MKTG WH TO ROLE MARKETING;
Correct Answer: B
B
Explanation:
: The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Access Control Privileges1
B
Explanation:
: The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Access Control Privileges1