SnowPro Core COF-C02 Dumps | Updated Sep 20, 2026 - VCETorrent
Master 2026 Latest The Questions SnowPro Core and Pass COF-C02 Real Exam!
Snowflake COF-C02 (SnowPro Core Certification) Certification Exam is designed for data professionals who want to validate their skills and knowledge in using the Snowflake data platform. SnowPro Core Certification Exam certification is ideal for data engineers, data architects, database administrators, data analysts, and data scientists who work with Snowflake. SnowPro Core Certification Exam certification exam is also suitable for IT professionals who want to expand their knowledge and skills in cloud data platforms.
Snowflake COF-C02 Certification Exam is a comprehensive exam that covers a range of topics related to Snowflake. COF-C02 exam is designed to test the knowledge and skills of professionals in areas such as data loading and unloading, user security, database objects, and performance tuning. SnowPro Core Certification Exam certification is perfect for professionals who want to showcase their expertise in Snowflake and stand out from the crowd.
NEW QUESTION # 544
Which function unloads data from a relational table to JSON?
- A. TRUNC(ID_NUMBER, 5)
- B. TRUNC<ID_NUMBER, -6)
- C. ID_NUMBER*100
- D. TO_CHAR<ID NUMBER)
Answer: D
Explanation:
To unload data from a relational table to JSON format, you can use the TO_CHAR function. This function converts a number to a character string, which can then be serialized into JSON format. While there isn't a direct function specifically named for unloading to JSON, converting the necessary fields to a string representation is a common step in preparing data for JSON serialization.
:
Snowflake Documentation: TO_CHAR Function
NEW QUESTION # 545
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. PUT
- B. COPY INTO <location>
- C. GET
- D. CREATE STREAM
Answer: A
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
NEW QUESTION # 546
A user has semi-structured data to load into Snowflake but is not sure what types of operations will need to be performed on the dat a. Based on this situation, what type of column does Snowflake recommend be used?
- A. OBJECT
- B. TEXT
- C. ARRAY
- D. VARIANT
Answer: D
NEW QUESTION # 547
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. GRANT MODIFY ON WAREHOUSE MKTG WH TO ROLE MARKETING;
- B. ALLOW RESIZE ON WAREHOUSE MKTG__WH TO USER MKTG__LEAD;
- C. GRANT MODIFY ON WAREHOUSE MKTG__WH TO USER MKTG__LEAD;
- D. GRANT OPERATE ON WAREHOUSE MKTG WH TO ROLE MARKETING;
Answer: A
NEW QUESTION # 548
When creating a custom role that will be allowed to create warehouses and databases, which system-defined role does Snowflake recommend also be assigned to the custom role?
- A. SECURITYADMIN
- B. ACCOUNTADMIN
- C. USERADMIN
- D. SYSADMIN
Answer: D
NEW QUESTION # 549
Using variables in Snowflake is denoted by using which SQL character?
- A. &
- B. #
- C. $
- D. @
Answer: C
Explanation:
VeryComprehensiveExplanation=InSnowflake,variablesaredenotedbyadollarsign(). Variables can be used in SQL statements where a literal constant is allowed, and they must be prefixed with a $ sign to distinguish them from bind values and column names.
NEW QUESTION # 550
How can a user validate if a micro-partition is pruning efficiently?
- A. By inspecting the statistics pane in the Query Profile
- B. By querying the INFORMATION_SCHEMA.TABLES view
- C. By using the SYSTEM$CLUSTERING_INFORMATION() function
- D. By inspecting the query details output in Snowsight
Answer: A
Explanation:
The correct answer is A. By inspecting the statistics pane in the Query Profile .
The Query Profile provides execution details for a query. To evaluate micro-partition pruning, users can inspect scan statistics, including partitions scanned versus total partitions. This helps determine whether Snowflake is efficiently skipping unnecessary micro-partitions.
Why A is correct:
The Query Profile statistics pane can show how many partitions were scanned and how many total partitions existed. Efficient pruning means Snowflake scanned only a small portion of the total relevant micro-partitions.
Example interpretation:
Partitions scanned: 100
Partitions total: 10,000
This indicates strong pruning because Snowflake skipped most micro-partitions.
Why the other options are incorrect:
B). Query details in Snowsight provide useful metadata such as duration, warehouse, and query text, but the Query Profile statistics pane is the best place to validate pruning efficiency.
C). SYSTEM$CLUSTERING_INFORMATION() gives clustering information for a table, such as clustering depth and overlap, but it does not directly show whether a specific query pruned efficiently.
D). INFORMATION_SCHEMA.TABLES provides table metadata, not query-level partition pruning details.
Official Snowflake documentation reference:
Snowflake documentation explains that Query Profile provides detailed execution information, including table scan statistics such as partitions scanned and partitions total. These statistics help validate pruning effectiveness.
Reference: Snowflake Documentation - Query Profile; Snowflake Documentation - Table scan statistics; Snowflake Documentation - Micro-partitions and pruning; SnowPro Core Study Guide - Performance Optimization.
NEW QUESTION # 551
Which Snowflake feature provides increased login security for users connecting to Snowflake that is powered by Duo Security service?
- A. Network policies
- B. OAuth
- C. Multi-Factor Authentication (MFA)
- D. Single Sign-On (SSO)
Answer: C
Explanation:
Multi-Factor Authentication (MFA) provides increased login security for users connecting to Snowflake. Snowflake's MFA is powered by Duo Security service, which adds an additional layer of security during the login process.
NEW QUESTION # 552
Which of the following describes a Snowflake stored procedure?
- A. They can be created as secure and hide the underlying metadata from the user.
- B. They can only access tables from a single database.
- C. They can be created to run with a caller's rights or an owner's rights.
- D. They can contain only a single SQL statement.
Answer: C
Explanation:
Snowflake stored procedures can be created to execute with the privileges of the role that owns the procedure (owner's rights) or with the privileges of the role that calls the procedure (caller's rights). This allows for flexibility in managing security and access control within Snowflake1.
NEW QUESTION # 553
What is the minimum Snowflake edition required to create a materialized view?
- A. Virtual Private Snowflake Edition
- B. Enterprise Edition
- C. Standard Edition
- D. Business Critical Edition
Answer: B
Explanation:
Materialized views require Enterprise Edition. To inquire about upgrading, please contact Snowflake Support
https://docs.snowflake.com/en/sql-reference/sql/create-materialized-view.html#:~:text=Materialized%20views%20require%20Enterprise%20Edition,upgrading%2C%20please%20contact%20Snowflake%20Support.
NEW QUESTION # 554
How can a user get the MOST detailed information about individual table storage details in Snowflake?
- A. SHOW EXTERNAL TABLES command
- B. TABLES view
- C. TABLE STORAGE METRICS view
- D. SHOW TABLES command
Answer: C
Explanation:
To obtain the most detailed information about individual table storage details in Snowflake, the TABLE STORAGE METRICS view is the recommended option. This view provides comprehensive metrics on storage usage, including data size, time travel size, fail-safe size, and other relevant storage metrics for each table. This level of detail is invaluable for monitoring, managing, and optimizing storage costs and performance.
References:
* Snowflake Documentation: Information Schema
NEW QUESTION # 555
How are network policies defined in Snowflake?
- A. They are a set of rules that control access to Snowflake accounts by specifying the IP addresses or ranges of IP addresses that are allowed to connect
- B. They are a set of rules that dictate how Snowflake accounts can be used between multiple users.
- C. They are a set of rules that define how data can be transferred between different Snowflake accounts within an organization.
- D. They are a set of rules that define the network routes within Snowflake.
Answer: A
Explanation:
to Snowflake.
NEW QUESTION # 556
Which of the following are characteristics of security in Snowflake?
- A. Support for HIPAA and GDPR compliance is available for UI Snowflake editions.
- B. Account and user authentication is only available with the Snowflake Business Critical edition.
- C. Private communication to internal stages is allowed in the Snowflake Enterprise edition and higher.
- D. Periodic rekeying of encrypted data is available with the Snowflake Enterprise edition and higher
Answer: D
NEW QUESTION # 557
When cloning tables, which information_schema view will show different columns for owned storage and referenced storage?
- A. DATABASE_ST0RAGE_USAGE_HISTORY
- B. TABLE_STORAGE_METRICS
- C. STAGE_STORAGE_USAGE_HISTORY
- D. STORAGE USAGE
Answer: B
Explanation:
* The TABLE_STORAGE_METRICS view in Snowflake provides details about table storage, including owned storage (storage exclusively used by the table) and referenced storage (shared storage due to cloning or Time Travel).
* This view helps distinguish between data directly owned by the table and data shared with other tables through features like cloning.
Why Other Options Are Incorrect:
* A. DATABASE_STORAGE_USAGE_HISTORY: Tracks storage usage at the database level, not specific to tables or cloning.
* B. STAGE_STORAGE_USAGE_HISTORY: Tracks storage usage for stages, not tables.
* D. STORAGE_USAGE: Provides high-level account storage details, not specific to cloning or tables.
References:
* TABLE_STORAGE_METRICS Documentation
NEW QUESTION # 558
Where is metadata management handled in Snowflake?
- A. Database Storage
- B. Cloud Services
- C. Query Processing
- D. Compute
Answer: B
NEW QUESTION # 559
How are micro-partitions enabled on Snowflake tables?
- A. Micro-partitioning requires the use of the search optimization service.
- B. Micro-partitioning requires a cluster key on a table.
- C. Micro-partitioning is automatically performed on a table.
- D. Micro-partitioning is defined by the user when a table is created.
Answer: C
Explanation:
* Snowflake uses micro-partitions automatically to store table data. A micro-partition is a contiguous unit of storage containing a subset of data from a table, enabling efficient data retrieval and performance optimization.
* Users do not need to manually define or enable micro-partitioning, as this process is automatically handled by Snowflake during data ingestion.
* Micro-partitions store metadata (e.g., column statistics, range of values) to optimize query performance.
Why Other Options Are Incorrect:
* A. Cluster key: Cluster keys help improve query performance for large datasets but are unrelated to enabling micro-partitions.
* C. Search optimization service: This is a separate feature for optimizing point lookups, not for enabling micro-partitions.
* D. User-defined micro-partitions: Snowflake does not allow users to define micro-partitions manually; it is fully automated.
References:
* Snowflake Micro-Partitioning Documentation
NEW QUESTION # 560
Which Snowflake virtual warehouse configuration enables horizontal scaling?
- A. Increasing the MIN_CLUSTER_C0UNT.
- B. Increasing the WAREHOSE_SIZE.
- C. Increasing the MAX_CONCURRENCY_LEVEL.
- D. Increasing the MAX_CLUSTER_COUNT.
Answer: D
Explanation:
* In Snowflake, horizontal scaling refers to adding more compute clusters to a virtual warehouse to handle higher query concurrency.
* MAX_CLUSTER_COUNT determines the maximum number of clusters that can run simultaneously for a multi-cluster warehouse.
* Increasing this value allows Snowflake to dynamically scale the number of clusters based on query demand.
Why Other Options Are Incorrect:
* A. WAREHOUSE_SIZE: Increasing warehouse size changes the compute capacity of a single cluster (vertical scaling), not horizontal scaling.
* C. MAX_CONCURRENCY_LEVEL: This controls query queuing and is unrelated to scaling the number of clusters.
* D. MIN_CLUSTER_COUNT: This sets the minimum number of clusters but does not affect maximum horizontal scaling.
References:
* Multi-Cluster Warehouses
* Virtual Warehouse Scaling
NEW QUESTION # 561
Which statement accurately describes how a virtual warehouse functions?
- A. All virtual warehouses share the same compute resources so performance degradation of one warehouse can significantly affect all the other warehouses.
- B. Each virtual warehouse is an independent compute cluster that shares compute resources with other warehouses.
- C. Increasing the size of a virtual warehouse will always improve data loading performance.
- D. Each virtual warehouse is a compute cluster composed of multiple compute nodes allocated by Snowflake from a cloud provider.
Answer: D
Explanation:
A virtual warehouse in Snowflake is an independent compute cluster that performs data processing tasks such as executing SQL queries. Each virtual warehouse is dynamically allocated by Snowflake from the cloud provider's resources and does not share compute resources with other warehouses. This architecture ensures that the performance of one warehouse does not impact the performance of another. Adjusting the size of a virtual warehouse affects its computational power by increasing or decreasing the number of compute nodes, which can improve the performance of data processing tasks depending on the workload.
Reference:
Snowflake Documentation: Understanding Virtual Warehouses
NEW QUESTION # 562
The Snowflake Search Optimization Services supports improved performance of which kind of query?
- A. Queries against a subset of columns in a table
- B. Queries against large tables where frequent DML occurs
- C. Selective point lookup queries
- D. Queries against tables larger than 1 TB
Answer: C
Explanation:
The Snowflake Search Optimization Service is designed to support improved performance for selective point lookup queries. These are queries that retrieve specific records from a database, often based on a unique identifier or a small set of criteria3.
NEW QUESTION # 563
Which Snowflake command can be used to unload the result of a query to a single file?
- A. Use COPY INTO <external stage> followed by a GET command to download the file.
- B. Use COPY INTO <internal stage> with SINGLE = TRUE followed by a GET command to download the file.
- C. Use COPY INTO <external stage> with SINGLE = TRUE followed by a PUT command to download the file.
- D. Use COPY INTO <internal stage> followed by a put command to download the file.
Answer: B
Explanation:
The Snowflake command to unload the result of a query to a single file is COPY INTO <internal stage> with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage
NEW QUESTION # 564
A tag object has been assigned to a table (TABLE_A) in a schema within a Snowflake database.
Which CREATE object statement will automatically assign the TABLE_A tag to a target object?
- A. CREATE MATERIALIZED VIEW <view name> AS SELECT * FROM TABLE A;
- B. CREATE VIEW <view_name> AS SELECT * FROM TABLE_A;
- C. CREATE TABLE <table_name> LIKE TABLE_A;
- D. CREATE TABLE <table_name> AS SELECT * FROM TABLE_A;
Answer: D
Explanation:
When a tag object is assigned to a table, using the statement CREATE TABLE <table_name> AS SELECT
* FROM TABLE_A will automatically assign the TABLE_A tag to the newly created table2.
NEW QUESTION # 565
What is the MINIMUM Snowflake edition required to use the periodic rekeying of micro-partitions?
- A. Enterprise
- B. Business Critical
- C. Virtual Private Snowflake
- D. Standard
Answer: A
Explanation:
Periodic rekeying of micro-partitions is a feature that requires the Enterprise Edition of Snowflake or higher. This feature is part of Snowflake's comprehensive approach to encryption key management, ensuring data security through regular rekeying1. Reference: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 566
Which type of join will list a I rows in the specified table, even if those rows have no match in the other table?
- A. Outer join
- B. Cross join
- C. Inner join
- D. Natural join
Answer: A
NEW QUESTION # 567
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