2026 100% Free COF-C03 Daily Practice Exam With 1187 Questions [Q239-Q264]

2026 100% Free COF-C03 Daily Practice Exam With 1187 Questions

COF-C03 exam torrent Snowflake study guide

NEW QUESTION # 239
How long can a data consumer who has a pre-signed URL access data files using Snowflake?

• A. Until the result_cache expires
• B. Until the expiration time is exceeded
• C. Until the retention_time is met
• D. Indefinitely

Answer: C

Explanation:
A data consumer who has a pre-signed URL can access data files using Snowflake until the expiration time is exceeded. The expiration time is set when the pre-signed URL is generated and determines how long the URL remains valid3.

NEW QUESTION # 240
How can a user validate if a micro-partition is pruning efficiently?

• A. By querying the INFORMATION_SCHEMA.TABLES view
• B. By using the SYSTEM$CLUSTERING_INFORMATION() function
• C. By inspecting the query details output in Snowsight
• D. By inspecting the statistics pane in the Query Profile

Answer: D

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 # 241
Which table function will return the output of a previously-run command?

• A. TASK_HISTORY
• B. RESULT_SCAN
• C. FLATTEN
• D. QUERY_HISTORY

Answer: B

NEW QUESTION # 242
When used with a SELECT statement in the COPY INTO < location > command, what statement will unload data from a table as JSON?

• A. TO_OBJECT
• B. TO_JSON
• C. OBJECT_CONSTRUCT
• D. PARSE_JSON

Answer: B

NEW QUESTION # 243
What are valid sub-clauses to the OVER clause for a window function? (Select TWO).

• A. LIMIT
• B. UNION ALL
• C. PARTITION BY
• D. GROUP BY
• E. ORDER BY

Answer: C,E

Explanation:
Valid sub-clauses to theOVERclause for a window function in SQL are:
C). ORDER BY: This clause specifies the order in which the rows in a partition are processed by the window function. It is essential for functions that depend on the row order, such as ranking functions.
D). PARTITION BY: This clause divides the result set into partitions to which the window function is applied.
Each partition is processed independently of other partitions, making it crucial for functions that compute values across sets of rows that share common characteristics.
These clauses are fundamental to defining the scope and order of data over which the window function operates, enabling complex analytical computations within SQL queries.
References:
Snowflake Documentation: Window Functions

NEW QUESTION # 244
Which setting will help control credit consumption for a virtual warehouse that needs to run in Auto-scale mode?

• A. Set the warehouse scaling policy to ECONOMY.
• B. Set the AUTO_SUSPEND property of the warehouse to 0.
• C. Set the MIN_CLUSTER_COUNT to 2 and set the MAX_CLUSTER_COUNT to 2.
• D. Set the MIN_CLUSTER_COUNT to 1.

Answer: A

Explanation:
The correct answer is A. Set the warehouse scaling policy to ECONOMY .
A multi-cluster warehouse can use Auto-scale mode to add or remove clusters based on query concurrency.
To help control credit consumption, Snowflake provides an Economy scaling policy.
Why A is correct:
The ECONOMY scaling policy is more conservative than the STANDARD scaling policy. It starts additional clusters only when Snowflake estimates there is enough query load to keep the new cluster busy for a meaningful amount of time. This helps reduce unnecessary cluster starts and can lower credit usage.
Why the other options are incorrect:
B). Setting MIN_CLUSTER_COUNT = 1 can help avoid running multiple clusters constantly, but it does not specifically control Auto-scale behavior as effectively as the Economy scaling policy.
C). Setting AUTO_SUSPEND = 0 disables automatic suspension, which can increase credit consumption.
D). Setting both minimum and maximum cluster count to 2 forces two clusters and removes Auto-scale flexibility. This can increase cost and does not control consumption efficiently.
Official Snowflake documentation reference:
Snowflake documentation explains that the Economy scaling policy conserves credits by starting additional clusters only when enough query load exists to justify them.
Reference: Snowflake Documentation - Multi-cluster warehouses; Snowflake Documentation - Scaling policy; SnowPro Core Study Guide - Snowflake Account and Resource Management.

NEW QUESTION # 245
Which of the following are benefits of micro-partitioning? (Select TWO)

• A. Micro-partitions cannot overlap in their range of values
• B. Micro-partitions can be defined on a schema-by-schema basis
• C. Micro-partitions are immutable objects that support the use of Time Travel.
• D. Rows are automatically stored in sorted order within micro-partitions
• E. Micro-partitions can reduce the amount of I/O from object storage to virtual warehouses

Answer: C,E

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 skipover 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-C03 ] SnowPro Core Certification Exam Study Guide
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html

NEW QUESTION # 246
What step can reduce data spilling in Snowflake?

• A. Using a common table expression (CTE) instead of a temporary table
• B. Increasing the amount of remote storage for the virtual warehouse
• C. Increasing the virtual warehouse maximum timeout limit
• D. Using a larger virtual warehouse

Answer: D

Explanation:
To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, whichcan accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide

NEW QUESTION # 247
A user wants to add additional privileges to the system-defined roles for their virtual warehouse. How does Snowflake recommend they accomplish this?

• A. Grant the additional privileges to a custom role.
• B. Grant the additional privileges to the ACCOUNTADMIN role.
• C. Grant the additional privileges to the SYSADMIN role.
• D. Grant the additional privileges to the ORGADMIN role.

Answer: A

Explanation:
Snowflake recommends enhancing the granularity and management of privileges by creating and utilizing custom roles. When additional privileges are needed beyond those provided by the system-defined roles for a virtual warehouse or any other resource, these privileges should be granted to a custom role. This approach allows for more precise control over access rights and the ability to tailor permissions to the specific needs of different user groups or applications within the organization, while also maintaining the integrity and security model of system-defined roles.
References:
Snowflake Documentation: Roles and Privileges

NEW QUESTION # 248
How does an authentication policy affect security in a Snowflake account?

• A. It restricts inbound access to Snowflake data.
• B. It controls how a client or user can access Snowflake.
• C. It provides users with the ability to use Single Sign-On, or SSO, to access Snowflake.
• D. It restricts outbound traffic from Snowflake to external network destinations.

Answer: B

Explanation:
The correct answer is A. It controls how a client or user can access Snowflake .
An authentication policy controls authentication behavior for users or accounts. It determines which authentication methods are allowed and can be used to enforce security requirements for access to Snowflake.
Why A is correct:
Authentication policies control how users or clients authenticate to Snowflake. They can be used to allow or restrict authentication methods, helping organizations enforce secure access standards.
Why the other options are incorrect:
B). Restricting inbound access to Snowflake is handled by network policies, not authentication policies.
C). Restricting outbound traffic from Snowflake to external network destinations is handled by external access integrations and network rules, not authentication policies.
D). SSO can be part of an authentication strategy, but an authentication policy is broader than only enabling SSO.
Official Snowflake documentation reference:
Snowflake documentation describes authentication policies as security objects that control authentication methods for users in a Snowflake account.
Reference: Snowflake Documentation - Authentication policies; Snowflake Documentation - Network policies; SnowPro Core Study Guide - Security and Access Control.

NEW QUESTION # 249
Snowflake users can create a resource monitor at which levels? (Select TWO).

• A. Pipe level
• B. Account level
• C. Cloud services level
• D. User level
• E. Virtual warehouse level

Answer: B,E

Explanation:
Resource monitors in Snowflake are tools used to track and control the consumption of compute resources, ensuring that usage stays within defined limits. These monitors can be created at the account level, allowing administrators to set overall resource consumption limits for the entire Snowflake account. Additionally, resource monitors can be set at the virtual warehouse level, enabling more granular control over the resources consumed by individual warehouses. This dual-level capability allows organizations to manage their Snowflake usage efficiently, preventing unexpected costs and optimizing performance.References: Snowflake Documentation on Resource Monitors Top of Form

NEW QUESTION # 250
Which object can be used to query the data loading history for the last 365 days for a Snowflake account?

• A. The SNOWFLAKE.ACCOUNT_USAGE.DATA_TRANSFER_HISTORY view
• B. The SNOWFLAKE.ACCOUNT_USAGE.COPY_HISTORY view
• C. The database INFORMATION_SCHEMA.COPY_HISTORY table function
• D. The database INFORMATION_SCHEMA.DATA_TRANSFER_HISTORY table function

Answer: B

NEW QUESTION # 251
A company needs to read multiple terabytes of data for an initial load as part of a Snowflake migration. The company can control the number and size of CSV extract files.
How does Snowflake recommend maximizing the load performance?

• A. Use an external tool to issue batched row-by-row inserts within BEGIN TRANSACTION and COMMIT commands.
• B. Produce a larger number of smaller files and process the ingestion with size Small virtual warehouses.
• C. Produce the largest files possible, reducing the overall number of files to process.
• D. Use auto-ingest Snowpipes to load large files in a serverless model.

Answer: C

Explanation:
Snowflake's documentation recommends producing the largest files possible for data loading, as larger files reduce the number of files to process and the overhead associated with handling many small files.This approach can maximize the load performance by leveraging Snowflake's ability to ingest large files efficiently1.References:[COF-C02] SnowPro Core Certification Exam Study Guide

NEW QUESTION # 252
What information is included in the output of an EXPLAIN_PLAN?

• A. The predicted time it will take to run the operation.
• B. The number of credits that the operation will consume.
• C. The total number of micro-partitions that the operation will reference.
• D. The type of data that will be returned when the operation is run.

Answer: C

Explanation:
The correct answer is D. The total number of micro-partitions that the operation will reference .
In Snowflake, EXPLAIN or EXPLAIN USING returns the logical execution plan for a SQL statement. The plan can include information about the operations Snowflake expects to perform, such as table scans, filters, joins, and estimated partitions involved in the query.
Why D is correct:
The explain plan can show partition-related information for table scan operations, including the number of partitions that may be scanned. This is useful for understanding how well Snowflake can prune micro- partitions.
Why the other options are incorrect:
A). EXPLAIN_PLAN does not provide a guaranteed predicted query runtime. Actual execution time depends on warehouse size, load, caching, pruning, and other runtime factors.
B). EXPLAIN_PLAN does not directly state the number of credits the query will consume. Credit consumption is based mainly on warehouse runtime and size.
C). EXPLAIN_PLAN describes the execution plan, not the actual result data type returned by the query.
Official Snowflake documentation reference:
Snowflake documentation explains that the EXPLAIN command displays the logical execution plan for a SQL statement. Query plans can include details about operations such as scans, joins, filters, and partitions.
Reference: Snowflake Documentation - EXPLAIN; Snowflake Documentation - Query Profile and query optimization; SnowPro Core Study Guide - Performance Optimization.
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NEW QUESTION # 253
Which languages requite that User-Defined Function (UDF) handlers be written inline? (Select TWO).

• A. Scala
• B. Java
• C. Javascript
• D. Python
• E. SQL

Answer: C,E

Explanation:
User-Defined Function (UDF) handlers must be written inline for Javascript and SQL.These languages allow the UDF logic to be included directly within the SQL statement that creates the UDF2.

NEW QUESTION # 254
A global company needs to share financial reports with managers who are in different regions. Managers should only see the data from their respective regions. How can these requirements be met with the least amount of operational overhead?

• A. Use row-level security on the source data table used to generate the reports.
• B. Apply Dynamic Data Masking based on a custom role assigned to each manager.
• C. Build individual reports for each manager.
• D. Create unique views for each region.

Answer: A

Explanation:
The correct answer is C. Use row-level security on the source data table used to generate the reports .
The requirement is to let managers see only rows for their respective regions. This is a row-filtering requirement, so Snowflake row-level security is the best fit.
Why C is correct:
Row-level security in Snowflake is implemented using row access policies. A row access policy can dynamically filter table rows based on the current user, current role, or mapping table logic.
Example concept:
CREATE ROW ACCESS POLICY region_policy
AS (region STRING) RETURNS BOOLEAN - >
region IN (
SELECT allowed_region
FROM manager_region_map
WHERE manager_role = CURRENT_ROLE()
);
This allows one shared table or report to serve multiple managers while enforcing region-specific visibility.
Why the other options are incorrect:
A). Creating unique views for each region increases operational overhead as regions or access rules change.
B). Building individual reports for each manager creates the highest operational overhead and is difficult to maintain.
D). Dynamic Data Masking hides or transforms column values. It does not filter rows by region.
Official Snowflake documentation reference:
Snowflake documentation describes row access policies as schema-level objects that determine whether a row is visible in a query result. They are used to implement row-level security.
Reference: Snowflake Documentation - Row access policies; Snowflake Documentation - Dynamic Data Masking; SnowPro Core Study Guide - Data Protection and Governance.

NEW QUESTION # 255
What can a directory table be used for? (Select TWO)

• A. Create a view for unstructured data.
• B. Process unstructured data using Snowflake system-defined functions.
• C. Retrieve a list of all unstructured files on a stage.
• D. Set an automatic refresh of the metadata for a directory table on internal and external stages.
• E. Remove files from a directory table using the GET command.

Answer: C,D

Explanation:
A directory table is a special Snowflake-managed table that stores metadata about unstructured data files (such as images, videos) in a stage.
* It supports automatic metadata refresh .
* It enables listing and querying file-level details .
Reference:
Snowflake Docs: Directory Tables

NEW QUESTION # 256
What kind of authentication do Snowpipe REST endpoints use?

• A. Single Sign-On (SSO)
• B. Username and password
• C. OAuth
• D. Key-based

Answer: D

Explanation:
Snowpipe uses key-based authentication for its REST endpoints. This involves generating and using a key pair (public and private keys) to securely authenticate API requests.
Generate Key Pair: Generate a public and private key pair.
Register Public Key: Register the public key with the Snowflake user that will be making the API requests.
Authenticate Requests: Use the private key to sign API requests sent to Snowpipe REST endpoints.
References:
Snowflake Documentation: Key Pair Authentication & Key Rotation
Snowflake Documentation: Using Snowpipe REST API

NEW QUESTION # 257
Which file function provides a URL with access to a file on a stage without the need for authentication and authorization?

• A. GET_PRESIGNED_URL
• B. BUILD_STAGE_FILE_URL
• C. BUILD_SCOPED_FILE_URL
• D. GET_RELATIVE_PATH

Answer: A

Explanation:
The GET_PRESIGNED_URL file function in Snowflake provides a URL with access to a file on a stage without requiring authentication and authorization. This is particularly useful for sharing data files stored in Snowflake stages with external parties securely and conveniently. The presigned URL generated by this function gives temporary access to the file, which expires after a specified duration.
Example usage of GET_PRESIGNED_URL:
SELECT GET_PRESIGNED_URL('<stage_name>', '<file_path>');
This function generates a URL that can be used to directly access a file in the stage, making it easier to share data without compromising security.
Top of Form

NEW QUESTION # 258
A user needs to create a materialized view in the schema MYDB.MYSCHEMA.
Which statements will provide this access?

• A. GRANT ROLE MYROLE TO USER USER1;CREATE MATERIALIZED VIEW ON SCHEMA
  MYDB.MYSCHEMA TO USER1;
• B. GRANT ROLE MYROLE TO USER USER1;CREATE MATERIALIZED VIEW ON SCHEMA
  MYDB.MYSCHEMA TO MYROLE;
• C. GRANT ROLE MYROLE TO USER USER1;CREATE MATERIALIZED VIEW ON SCHEMA
  MYDB.MYSCHEMA TO ROLE MYROLE;
• D. GRANT ROLE MYROLE TO USER USER1;CREATE MATERIALIZED VIEW ON SCHEMA
  MYDB.MYSCHEMA TO USER USER1;

Answer: B

Explanation:
In Snowflake, to create a materialized view, the user must have the necessary privileges on the schema where the view will be created. These privileges are granted through roles, not directly to individual users.
Therefore, the correct process is to grant the role to the user and then grant the privilege to create the materialized view to the role itself.
The statement GRANT ROLE MYROLE TO USER USER1; grants the specified role to the user, allowing them to assume that role and exercise its privileges. The subsequent statement CREATE MATERIALIZED VIEW ON SCHEMA MYDB.MYSCHEMA TO MYROLE; grants the privilege to create a materialized view within the specified schema to the role MYROLE. Any user who has been granted MYROLE can then create materialized views in MYDB.MYSCHEMA.
References:
Snowflake Documentation on Roles
Snowflake Documentation on Materialized Views

NEW QUESTION # 259
How can a 5 GB table be downloaded into a single file MOST efficiently?

• A. Set the SINGLE parameter to TRUE.
• B. Keep the default MAX_FILE_SIZE to 16 MB
• C. Set the default MAX_FILE_SI2E to 5 G
• D. Use a regular expression in the stage specifications of the COPY command.

Answer: A

Explanation:
To download a 5 GB table into a single file most efficiently in Snowflake, you should set the SINGLE parameter to TRUE. This parameter ensures that the COPY INTO command outputs the result into a single file, regardless of the file size. This approach is more efficient than relying on the default MAX_FILE_SIZE setting, which would split the output into multiple files.
References:
Snowflake Documentation: COPY INTO <location>

NEW QUESTION # 260
Which snowflake objects will incur both storage and cloud compute charges? (Select TWO)

• A. Clustered table
• B. Materialized view
• C. Sequence
• D. Transient table
• E. Secure view

Answer: B,D

Explanation:
In Snowflake, both materialized views and transient tables will incur storage charges because they store data.
They will also incur compute charges when queries are run against them, as compute resources are used to process the queries. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide

NEW QUESTION # 261
What is the name of the SnowSQLfile that can store connection information?

• A. history
• B. snowsql.pubkey
• C. snowsqLcnf
• D. config

Answer: D

Explanation:
The SnowSQL file that can store connection information is named 'config'. It is used to store user credentials and connection details for easy access to Snowflake instances.References:Based on general database knowledge as of 2021.

NEW QUESTION # 262
Which command will indicate whether a materialized view is being used in a query?

• A. EXPLAIN
• B. SHOW
• C. USE
• D. DESCRIBE

Answer: A

NEW QUESTION # 263
When loading data into Snowflake, how should the data be organized?

• A. Into files of maximum size of 4 GB of compressed data per file
• B. Into single files with 100-250 MB of compressed data per file
• C. Into single files with 1-100 MB of compressed data per file
• D. Into files of maximum size of 1 GB of compressed data per file

Answer: B

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-C03 ] SnowPro Core Certification Exam Study Guide

NEW QUESTION # 264
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