Snowflake COF-C02 Practice Exams
Last updated on Oct 23,2026- Exam Code: COF-C02
- Exam Name: SnowPro Core Certification Exam
- Certification Provider: Snowflake
- Latest update: Oct 23,2026
Question #171
Which statement accurately describes a characteristic of a materialized view?
- A . A materialized view can query only a single table.
- B . Data accessed through materialized views can be stale.
- C . Materialized view refreshes need to be maintained by the user.
- D . Querying a materialized view is slower than executing a query against the base table of the view.
Correct Answer: B
B
Explanation:
A characteristic of a materialized view is that the data accessed through it can be stale. This is because the data in a materialized view may not reflect the latest changes in the base tables until the view is refreshed
B
Explanation:
A characteristic of a materialized view is that the data accessed through it can be stale. This is because the data in a materialized view may not reflect the latest changes in the base tables until the view is refreshed
Question #172
What is the recommended compressed file size range for continuous data loads using Snowpipe?
- A . 8-16 MB
- B . 16-24 MB
- C . 10-99 MB
- D . 100-250 MB
Correct Answer: D
D
Explanation:
For continuous data loads using Snowpipe, the recommended compressed file size range is between 100-250 MB. This size range is suggested to optimize the number of parallel operations for a load and to avoid size limitations, ensuring efficient and cost-effective data loading
D
Explanation:
For continuous data loads using Snowpipe, the recommended compressed file size range is between 100-250 MB. This size range is suggested to optimize the number of parallel operations for a load and to avoid size limitations, ensuring efficient and cost-effective data loading
Question #173
What persistent data structures are used by the search optimization service to improve the performance of point lookups?
- A . Micro-partitions
- B . Clustering keys
- C . Equality searches
- D . Search access paths
Correct Answer: D
D
Explanation:
The search optimization service in Snowflake uses persistent data structures known as search access paths to improve the performance of point lookups. These structures enable efficient retrieval of data by reducing the amount of data scanned during queries.
Search Access Paths:
Search access paths are special indexing structures maintained by the search optimization service.
They store metadata about the distribution of data within tables, enabling faster lookups for specific values.
Point Lookups:
Point lookups involve searching for a specific value within a column.
By leveraging search access paths, Snowflake can quickly locate the exact micro-partition containing the value, minimizing the amount of data scanned.
Performance Improvement:
The use of search access paths significantly reduces query execution time for point lookups.
This is especially beneficial for large tables where scanning all micro-partitions would be computationally expensive.
References:
Snowflake Documentation: Search Optimization Service
Snowflake Documentation: Understanding Search Access Paths
D
Explanation:
The search optimization service in Snowflake uses persistent data structures known as search access paths to improve the performance of point lookups. These structures enable efficient retrieval of data by reducing the amount of data scanned during queries.
Search Access Paths:
Search access paths are special indexing structures maintained by the search optimization service.
They store metadata about the distribution of data within tables, enabling faster lookups for specific values.
Point Lookups:
Point lookups involve searching for a specific value within a column.
By leveraging search access paths, Snowflake can quickly locate the exact micro-partition containing the value, minimizing the amount of data scanned.
Performance Improvement:
The use of search access paths significantly reduces query execution time for point lookups.
This is especially beneficial for large tables where scanning all micro-partitions would be computationally expensive.
References:
Snowflake Documentation: Search Optimization Service
Snowflake Documentation: Understanding Search Access Paths
Question #174
Which function determines the kind of value stored in a VARIANT column?
- A . CHECK_JSON
- B . IS_ARRAY
- C . IS_JSON
- D . TYPEOF
Correct Answer: D
D
Explanation:
The function used to determine the kind of value stored in a VARIANT column in Snowflake is TYPEOF.
Understanding VARIANT Data Type:
VARIANT is a flexible data type in Snowflake that can store semi-structured data, such as JSON, Avro, and XML.
This data type can hold values of different types, including strings, numbers, objects, arrays, and more.
Using TYPEOF Function:
The TYPEOF function returns the data type of the value stored in a VARIANT column.
It helps in identifying the type of data, which is crucial for processing and transforming semi-structured data accurately.
Example Usage:
SELECT TYPEOF(variant_column)
FROM my_table;
This query retrieves the type of data stored in variant_column for each row in my_table.
Possible return values include ‘OBJECT’, ‘ARRAY’, ‘STRING’, ‘NUMBER’, etc.
Benefits:
Simplifies data processing: Knowing the data type helps in applying appropriate transformations and validations.
Enhances query accuracy: Ensures that operations on VARIANT columns are performed correctly based on the data type.
References:
Snowflake Documentation: TYPEOF
Snowflake Documentation: VARIANT Data Type
D
Explanation:
The function used to determine the kind of value stored in a VARIANT column in Snowflake is TYPEOF.
Understanding VARIANT Data Type:
VARIANT is a flexible data type in Snowflake that can store semi-structured data, such as JSON, Avro, and XML.
This data type can hold values of different types, including strings, numbers, objects, arrays, and more.
Using TYPEOF Function:
The TYPEOF function returns the data type of the value stored in a VARIANT column.
It helps in identifying the type of data, which is crucial for processing and transforming semi-structured data accurately.
Example Usage:
SELECT TYPEOF(variant_column)
FROM my_table;
This query retrieves the type of data stored in variant_column for each row in my_table.
Possible return values include ‘OBJECT’, ‘ARRAY’, ‘STRING’, ‘NUMBER’, etc.
Benefits:
Simplifies data processing: Knowing the data type helps in applying appropriate transformations and validations.
Enhances query accuracy: Ensures that operations on VARIANT columns are performed correctly based on the data type.
References:
Snowflake Documentation: TYPEOF
Snowflake Documentation: VARIANT Data Type
Question #175
Which function will convert semi-structured data to a relational data representation?
- A . PARSE JSON
- B . FLATTEN
- C . OBJECT_CONSTRUCT
- D . OBJECT_AGG
Correct Answer: B
Question #176
How does Snowflake utilize clustering information to improve query performance?
- A . It prunes unnecessary micro-partitions based on clustering metadata.
- B . It compresses the data within micro-partitions for faster querying.
- C . It automatically allocates additional resources to improve query execution.
- D . It organizes clustering information to speed-up data retrieval from storage
Correct Answer: A
A
Explanation:
Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.
Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.
Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.
References:
Snowflake Documentation on Clustering
Snowflake Documentation on Micro-partition Pruning
A
Explanation:
Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.
Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.
Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.
References:
Snowflake Documentation on Clustering
Snowflake Documentation on Micro-partition Pruning
Question #177
Which data types are supported by Snowflake when using semi-structured data? (Choose two.)
- A . VARIANT
- B . VARRAY
- C . STRUCT
- D . ARRAY
- E . QUEUE
Correct Answer: A, D
A, D
Explanation:
Snowflake supports the VARIANT and ARRAY data types for semi-structured data. VARIANT can store values of any other type, including OBJECT and ARRAY, making it suitable for semi-structured data formats like JSON. ARRAY is used to store an ordered list of elements
A, D
Explanation:
Snowflake supports the VARIANT and ARRAY data types for semi-structured data. VARIANT can store values of any other type, including OBJECT and ARRAY, making it suitable for semi-structured data formats like JSON. ARRAY is used to store an ordered list of elements
Question #178
When cloning a database, what is cloned with the database? (Choose two.)
- A . Privileges on the database
- B . Existing child objects within the database
- C . Future child objects within the database
- D . Privileges on the schemas within the database
- E . Only schemas and tables within the database
Correct Answer: A, B
A, B
Explanation:
When cloning a database in Snowflake, the clone includes all privileges on the database as well as existing child objects within the database, such as schemas, tables, views, etc. However, it does not include future child objects or privileges on schemas within the database2.
References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation
A, B
Explanation:
When cloning a database in Snowflake, the clone includes all privileges on the database as well as existing child objects within the database, such as schemas, tables, views, etc. However, it does not include future child objects or privileges on schemas within the database2.
References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation
Question #179
In the query profiler view for a query, which components represent areas that can be used to help optimize query performance? (Select TWO)
- A . Bytes scanned
- B . Bytes sent over the network
- C . Number of partitions scanned
- D . Percentage scanned from cache
- E . External bytes scanned
Correct Answer: A, C
A, C
Explanation:
In the query profiler view, the components that represent areas that can be used to help optimize query performance include ‘Bytes scanned’ and ‘Number of partitions scanned’. ‘Bytes scanned’ indicates the total amount of data the query had to read and is a direct indicator of the query’s efficiency. Reducing the bytes scanned can lead to lower data transfer costs and faster query execution. ‘Number of partitions scanned’ reflects how well the data is clustered; fewer partitions scanned typically means better performance because the system can skip irrelevant data more effectively.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Query Profiling1
A, C
Explanation:
In the query profiler view, the components that represent areas that can be used to help optimize query performance include ‘Bytes scanned’ and ‘Number of partitions scanned’. ‘Bytes scanned’ indicates the total amount of data the query had to read and is a direct indicator of the query’s efficiency. Reducing the bytes scanned can lead to lower data transfer costs and faster query execution. ‘Number of partitions scanned’ reflects how well the data is clustered; fewer partitions scanned typically means better performance because the system can skip irrelevant data more effectively.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Query Profiling1
Question #180
Which command can be used to unload data into an external named stage in Snowflake?
- A . PUT
- B . CREATE <object>
- C . COPY INTO <table>
- D . COPY INTO <location>
Correct Answer: D