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 #301
Which virtual warehouse consideration can help lower compute resource credit consumption?
- A . Setting up a multi-cluster virtual warehouse
- B . Resizing the virtual warehouse to a larger size
- C . Automating the virtual warehouse suspension and resumption settings
- D . Increasing the maximum cluster count parameter for a multi-cluster virtual warehouse
Correct Answer: C
C
Explanation:
One key strategy to lower compute resource credit consumption in Snowflake is by automating the suspension and resumption of virtual warehouses. Virtual warehouses consume credits when they are running, and managing their operational times effectively can lead to significant cost savings.
C
Explanation:
One key strategy to lower compute resource credit consumption in Snowflake is by automating the suspension and resumption of virtual warehouses. Virtual warehouses consume credits when they are running, and managing their operational times effectively can lead to significant cost savings.
Question #302
What objects in Snowflake are supported by Dynamic Data Masking? (Select TWO).’
- A . Views
- B . Materialized views
- C . Tables
- D . External tables
- E . Future grants
Correct Answer: A, C
A, C
Explanation:
Dynamic Data Masking in Snowflake supports tables and views. These objects can have masking policies applied to their columns to dynamically mask data at query time3.
A, C
Explanation:
Dynamic Data Masking in Snowflake supports tables and views. These objects can have masking policies applied to their columns to dynamically mask data at query time3.
Question #303
For non-materialized views, what column in Information Schema and Account Usage identifies whether a view is secure or not?
- A . CHECK_OPTION
- B . IS_SECURE
- C . IS_UPDATEABLE
- D . TABLE_NAME
Correct Answer: B
B
Explanation:
In the Information Schema and Account Usage, the column that identifies whether a view is secure or not is IS_SECURE2.
B
Explanation:
In the Information Schema and Account Usage, the column that identifies whether a view is secure or not is IS_SECURE2.
Question #304
What statistical information in a Query Profile indicates that the query is too large to fit in memory? (Select TWO).
- A . Bytes spilled to local cache.
- B . Bytes spilled to local storage.
- C . Bytes spilled to remote cache.
- D . Bytes spilled to remote storage.
- E . Bytes spilled to remote metastore.
Correct Answer: A, B
A, B
Explanation:
In a Query Profile, the statistical information that indicates a query is too large to fit in memory includes bytes spilled to local cache and bytes spilled to local storage. These metrics suggest that the working data set of the query exceeded the memory available on the warehouse nodes, causing intermediate results to be written to disk
A, B
Explanation:
In a Query Profile, the statistical information that indicates a query is too large to fit in memory includes bytes spilled to local cache and bytes spilled to local storage. These metrics suggest that the working data set of the query exceeded the memory available on the warehouse nodes, causing intermediate results to be written to disk
Question #305
What is a responsibility of Snowflake’s virtual warehouses?
- A . Infrastructure management
- B . Metadata management
- C . Query execution
- D . Query parsing and optimization
- E . Management of the storage layer
Correct Answer: C
C
Explanation:
The primary responsibility of Snowflake’s virtual warehouses is to execute queries. Virtual warehouses are one of the key components of Snowflake’s architecture, providing the compute power required to perform data processing tasks such as running SQL queries, performing joins, aggregations, and other data manipulations.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Virtual Warehouses1
C
Explanation:
The primary responsibility of Snowflake’s virtual warehouses is to execute queries. Virtual warehouses are one of the key components of Snowflake’s architecture, providing the compute power required to perform data processing tasks such as running SQL queries, performing joins, aggregations, and other data manipulations.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Virtual Warehouses1
Question #306
Which of the following are best practices for loading data into Snowflake? (Choose three.)
- A . Aim to produce data files that are between 100 MB and 250 MB in size, compressed.
- B . Load data from files in a cloud storage service in a different region or cloud platform from the service or region containing the Snowflake account, to save on cost.
- C . Enclose fields that contain delimiter characters in single or double quotes.
- D . Split large files into a greater number of smaller files to distribute the load among the compute
resources in an active warehouse. - E . When planning which warehouse(s) to use for data loading, start with the largest warehouse possible.
- F . Partition the staged data into large folders with random paths, allowing Snowflake to determine the best way to load each file.
Correct Answer: A, C, D
A, C, D
Explanation:
Best practices for loading data into Snowflake include aiming for data file sizes between 100 MB and 250 MB when compressed, as this size is optimal for parallel processing and minimizes overhead. Enclosing fields with delimiter characters in quotes ensures proper field recognition during the load process. Splitting large files into smaller ones allows for better distribution of the load across compute resources, enhancing performance and efficiency.
A, C, D
Explanation:
Best practices for loading data into Snowflake include aiming for data file sizes between 100 MB and 250 MB when compressed, as this size is optimal for parallel processing and minimizes overhead. Enclosing fields with delimiter characters in quotes ensures proper field recognition during the load process. Splitting large files into smaller ones allows for better distribution of the load across compute resources, enhancing performance and efficiency.
Question #307
What optional properties can a Snowflake user set when creating a virtual warehouse? (Select TWO).
- A . Auto-suspend
- B . Cache size
- C . Default role
- D . Resource monitor
- E . Storage size
Correct Answer: D, A
D, A
Explanation:
When creating a virtual warehouse in Snowflake, users have the option to set several properties to manage its behavior and resource usage. Two of these optional properties are Auto-suspend and Resource monitor.
Auto-suspend: This property defines the period of inactivity after which the warehouse will automatically suspend. This helps in managing costs by stopping the warehouse when it is not in use.
CREATE WAREHOUSE my_warehouse
WITH WAREHOUSE_SIZE = ‘XSMALL’
AUTO_SUSPEND = 300; — Auto-suspend after 5 minutes of inactivity
Resource monitor: Users can assign a resource monitor to a warehouse to control and limit the amount of credit usage. Resource monitors help in setting quotas and alerts for warehouse usage.
CREATE WAREHOUSE my_warehouse
WITH WAREHOUSE_SIZE = ‘XSMALL’
RESOURCE_MONITOR = ‘my_resource_monitor’;
References:
Snowflake Documentation: Creating Warehouses
Snowflake Documentation: Resource Monitors
D, A
Explanation:
When creating a virtual warehouse in Snowflake, users have the option to set several properties to manage its behavior and resource usage. Two of these optional properties are Auto-suspend and Resource monitor.
Auto-suspend: This property defines the period of inactivity after which the warehouse will automatically suspend. This helps in managing costs by stopping the warehouse when it is not in use.
CREATE WAREHOUSE my_warehouse
WITH WAREHOUSE_SIZE = ‘XSMALL’
AUTO_SUSPEND = 300; — Auto-suspend after 5 minutes of inactivity
Resource monitor: Users can assign a resource monitor to a warehouse to control and limit the amount of credit usage. Resource monitors help in setting quotas and alerts for warehouse usage.
CREATE WAREHOUSE my_warehouse
WITH WAREHOUSE_SIZE = ‘XSMALL’
RESOURCE_MONITOR = ‘my_resource_monitor’;
References:
Snowflake Documentation: Creating Warehouses
Snowflake Documentation: Resource Monitors
Question #308
Which statements reflect key functionalities of a Snowflake Data Exchange? (Choose two.)
- A . If an account is enrolled with a Data Exchange, it will lose its access to the Snowflake Marketplace.
- B . A Data Exchange allows groups of accounts to share data privately among the accounts.
- C . A Data Exchange allows accounts to share data with third, non-Snowflake parties.
- D . Data Exchange functionality is available by default in accounts using the Enterprise edition or higher.
- E . The sharing of data in a Data Exchange is bidirectional. An account can be a provider for some datasets and a consumer for others.
Correct Answer: B, E
B, E
Explanation:
A Snowflake Data Exchange allows groups of accounts to share data privately among the accounts (B), and it supports bidirectional sharing, meaning an account can be both a provider and a consumer of data (E). This facilitates secure and governed data collaboration within a selected group3.
B, E
Explanation:
A Snowflake Data Exchange allows groups of accounts to share data privately among the accounts (B), and it supports bidirectional sharing, meaning an account can be both a provider and a consumer of data (E). This facilitates secure and governed data collaboration within a selected group3.
Question #309
Which function is used to unload a relational table into a JSON file*?
- A . PARSE_JSON
- B . JSON_EXTRACT_PATH_TEXT
- C . OBJECT_CONSTRUCT
- D . TO_JSON
Correct Answer: D
D
Explanation:
The TO_JSON function in Snowflake is used to convert a relational table or individual rows into JSON format. This function is helpful for exporting data in JSON format.
Using TO_JSON Function:
SELECT TO_JSON(OBJECT_CONSTRUCT(*))
FROM my_table;
Exporting Data: The TO_JSON function converts the table rows into JSON format, which can then be exported to a file.
References:
Snowflake Documentation: TO_JSON Function
Snowflake Documentation: Exporting Data
D
Explanation:
The TO_JSON function in Snowflake is used to convert a relational table or individual rows into JSON format. This function is helpful for exporting data in JSON format.
Using TO_JSON Function:
SELECT TO_JSON(OBJECT_CONSTRUCT(*))
FROM my_table;
Exporting Data: The TO_JSON function converts the table rows into JSON format, which can then be exported to a file.
References:
Snowflake Documentation: TO_JSON Function
Snowflake Documentation: Exporting Data
Question #310
Which data type can be used for floating-point numbers without losing precision?
- A . BINARY
- B . VARIANT
- C . INTEGER
- D . DOUBLE
Correct Answer: D
D
Explanation:
In Snowflake, the DOUBLE data type is used for floating-point numbers and can represent a wide range of values without losing precision. This data type is ideal for storing numerical values that require decimal precision.
Define the Column: When creating a table, specify the column with the DOUBLE data type to store floating-point numbers.
CREATE TABLE example_table (
id INTEGER,
value DOUBLE
);
Insert Data: Insert floating-point numbers into the DOUBLE column.
INSERT INTO example_table (id, value) VALUES (1, 123.456);
References:
Snowflake Documentation: Data Types
Snowflake Documentation: Numeric Data Types
D
Explanation:
In Snowflake, the DOUBLE data type is used for floating-point numbers and can represent a wide range of values without losing precision. This data type is ideal for storing numerical values that require decimal precision.
Define the Column: When creating a table, specify the column with the DOUBLE data type to store floating-point numbers.
CREATE TABLE example_table (
id INTEGER,
value DOUBLE
);
Insert Data: Insert floating-point numbers into the DOUBLE column.
INSERT INTO example_table (id, value) VALUES (1, 123.456);
References:
Snowflake Documentation: Data Types
Snowflake Documentation: Numeric Data Types