COF-C02 Snowflake SnowPro Core Certification Exam Free Practice Exam Questions (2026 Updated)

To ensure a data consumer has access to the required objects, a data provider can enable the data sharing feature and validate that the consumer can access the views or tables shared with them. References: Based on general data sharing practices in cloud services as of 2021.

The Snowflake Account_Usage.Metering_History view is used to gather the hourly credit usage for an account. This view provides details on the credits consumed by various services within Snowflake for the last 365 days1.

The first user assigned to a new Snowflake account, typically with the ACCOUNTADMIN role, should create at least one additional user with the USERADMIN administrative privilege. This role is responsible for creating and managing users and roles within the Snowflake account. References: Access control considerations | Snowflake Documentation

The COPY INTO < table > command in Snowflake supports transformations such as casting using a SELECT statement. This allows for the transformation of data types as the data is being loaded into the table, which can be particularly useful when the data types in the source files do not match the data types in the target table

When a database is cloned in Snowflake, it does not retain any privileges that were granted on the source object. The clone will need to have privileges reassigned as necessary for users to access it. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 The kind of Snowflake table that stores file-level metadata for each file in a stage is a directory table. A directory table is an implicit object layered ona stage and stores file-level metadata about the data files in the stage3.

The OPERATE privilege is required for a role to resume a suspended warehouse if auto-resume is not enabled. This privilege allows the role to start, stop, suspend, or resume a virtual warehouse3.

To find out which numeric values are present in both tables, the INTERSECT set operator should be used. This operator returns rows from one query’s result set which also appear in another query’s result set, effectively finding the common elements between the two tables45.

 Clustering in Snowflake refers to the organization of data within micro-partitions, which are contiguous units of storage within Snowflake tables. Clustering keys can be defined to co-locate similar rows in the same micro-partitions, improving scan efficiency and query performance12.

 Automatic Clustering is a feature that allows users to control the organization of data within micro-partitions in Snowflake. By defining clustering keys, Snowflake can automatically reorganize the data in micro-partitions to optimize query performance1.

The Snowflake object that helps evaluate virtual warehouse performance impacted by query queuing is the Information_schema.warehouse_load_history. This view provides historical data about the load on a warehouse, including the average number of queries that were running or queued within a specific interval, which can be used to assess performance and identify potential issues with query queuing3.

Snowflake recommends creating objects with a role that has the necessary privileges and is not overly permissive. SYSADMIN is typically used for managing system-level objects and operations. Creating objects with a custom role and granting this role to SYSADMIN allows for more granular control and adherence to the principle of least privilege. References: Based on best practices for database object ownership and role management.

 Snowflake’s billing for virtual warehouses is per-second, with a minimum of 60 seconds for each time the warehouse is started or resumed. Therefore, if a warehouse runs for 61 seconds, it is billed for 61 seconds. If it is then shut down and restarted, running for anadditional 30 seconds, it is billed for another 60 seconds (the minimum charge for a restart), totaling 121 seconds2

The Snowflake feature that allows for small volumes of data to be continuously loaded into Snowflake and incrementally made available for analysis is Snowpipe. Snowpipe is designed for near-real-time data loading, enabling data to be loaded as soon as it’s available in the storage layer3

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. Thisapproach can maximize the load performance by leveraging Snowflake’s ability to ingest large files efficiently1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake retains information in the ACCESS HISTORY view for 365 days. This allows users to query the access history of Snowflake objects within the last year1.

SnowSQL is the Command Line Interface (CLI) client provided by Snowflake for executing SQL queries and performing various tasks. It allows users to connect to their Snowflake accounts and interact with the Snowflake data warehouse.

Installation:SnowSQL can be downloaded and installed on various operating systems.

Configuration:Users need to configure SnowSQL with their Snowflake account credentials.

Usage:Once configured, users can run SQL queries, manage data, and perform administrative tasks through the CLI.

Snowflake’s Time Travel feature allows users to access historical data within a specific period. This feature supports the restoration of various objects, including databases and schemas, to their previous states. Time Travel can be used for recovering dropped objects, undoing accidental changes, or analyzing data changes over time. However, it does not support user or role objects like Users and Roles, or compute resources like Virtual Warehouses.

The correct answer is C. Rotation .

Key-pair authentication uses a public/private key pair to authenticate users or applications. To improve security, keys should be replaced periodically or when there is a possibility that a key has been exposed. This process is called key rotation .

Why C is correct:

Key rotation allows an organization to replace active keys with new keys. In Snowflake, users can have public keys assigned for key-pair authentication, and rotating keys helps maintain secure authentication practices.

Why the other options are incorrect:

A. Authentication is the overall process of verifying identity, not the specific feature for replacing keys.

B. Passkeys are a different authentication mechanism and are not the feature used to replace active key-pair authentication keys.

D. Time-based one-time passcodes are associated with MFA, not key-pair replacement.

Official Snowflake documentation reference:

Snowflake documentation describes key-pair authentication and includes key rotation as a security practice for replacing public/private key pairs.

TheMIN_DATA_RETENTION_TIME_IN_DAYSparameter is set at theAccountlevel in Snowflake. This parameter specifies the minimum number of days Snowflake retains the historical data for time travel, which allows users to access and query data as it existed at previous points in time.

Here ' s how to understand and adjust this parameter:

Purpose of MIN_DATA_RETENTION_TIME_IN_DAYS:This parameter is crucial for managing data lifecycle and compliance requirements within Snowflake. It determines the minimum time frame for which you can perform operations like restoring deleted objects or accessing historical versions of data.

Setting the Parameter:Only account administrators can set or modify this parameter. It is done at the account level, impacting all databases and schemas within the account. The setting can be adjusted based on the organization ' s data retention policy.

Adjusting the Parameter:

To view the current setting, use:

SHOWPARAMETERSLIKE ' MIN_DATA_RETENTION_TIME_IN_DAYS ' ;

To change the setting, an account administrator can execute:

ALTERACCOUNTSETMIN_DATA_RETENTION_TIME_IN_DAYS = < number_of_days > ;

To minimize Continuous Data Protection (CDP) costs when dealing with large, high-churn dimension tables in Snowflake, using transient tables is a recommended approach.

Transient Tables: These are designed for data that does not require fail-safe protection. They provide the benefit of reducing costs associated with continuous data protection since they do not have the seven-day Fail-safe period that is mandatory for permanent tables.

Periodic Copying to Permanent Tables: By periodically copying data from transient tables to permanent tables, you can achieve a balance between data protection and cost-efficiency. Permanent tables offer the extended data protection features, including Time Travel and Fail-safe, butthese features can be applied selectively rather than continuously, reducing the overall CDP costs.

Snowflake Documentation on Transient Tables

Snowflake Documentation on Time Travel & Fail-safe

Column-level security in Snowflake enhances data protection by restricting access and applying masking policies to sensitive data at the column level.

Limiting Access Based on User Privileges:

Column-level security allows administrators to define which users or roles have access to specific columns within a table.

This ensures that sensitive data is only accessible to authorized personnel, thereby reducing the risk of data breaches.

Application of Masking Policies:

Masking policies can be applied to columns to obfuscate sensitive data.

For example, credit card numbers can be masked to show only the last four digits, protecting the full number from being exposed.

Snowflake Documentation: Column-Level Security

Snowflake Documentation: Dynamic Data Masking

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.

The correct answer is B. Copilot provides assistance when creating and interpreting queries .

Snowflake Copilot is an AI-assisted feature that helps users work with Snowflake more efficiently. It can assist with writing SQL, understanding queries, and interpreting query-related information.

Why B is correct:

Copilot is designed to help users create, refine, and understand SQL queries using natural language assistance.

Why the other options are incorrect:

A. Query optimization is primarily handled by Snowflake’s optimizer and performance features, not Copilot.

C. Copilot does not automatically rewrite table schemas based on inferred data patterns.

D. Copilot is not primarily a feature for executing stored procedures from prompts.

Official Snowflake documentation reference:

Snowflake documentation describes Snowflake Copilot as an AI assistant for helping users generate, understand, and work with SQL and Snowflake data workflows.

The correct answer is A. VARCHAR .

A clustering key can be defined on supported table columns or expressions to help Snowflake organize micro-partitions and improve pruning for certain query patterns. Standard scalar data types such as VARCHAR are supported.

Why A is correct:

VARCHAR is a standard scalar data type and can be used in a clustering key.

Example:

CREATE TABLE customer_orders (

customer_id VARCHAR,

order_date DATE,

amount NUMBER

)

CLUSTER BY (customer_id);

Why the other options are incorrect:

B. VARIANT stores semi-structured data. Clustering directly on raw semi-structured columns is not the best answer here; clustering keys should use supported scalar columns or expressions.

C. OBJECT is a semi-structured data type and is not the correct choice for a clustering key column in this question.

D. GEOGRAPHY is not the correct supported clustering key data type in this context.

Official Snowflake documentation reference:

Snowflake documentation explains that clustering keys can be defined using table columns or expressions, and standard scalar columns such as string, number, and date/time columns are commonly used for clustering.

Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.

The correct answer is B. Restrict access to Snowflake to known networks using a network policy .

A network policy improves account access security by allowing or blocking access based on IP address ranges. This helps ensure that users can connect to Snowflake only from approved networks.

Why B is correct:

Network policies can restrict inbound access to Snowflake by defining allowed and blocked IP address ranges. This is a direct security control for user access to a Snowflake account.

Example concept:

CREATE NETWORK POLICY corp_network_policy

ALLOWED_IP_LIST = ( ' 203.0.113.0/24 ' );

ALTER ACCOUNT SET NETWORK_POLICY = corp_network_policy;

Why the other options are incorrect:

A. A UDF is not the correct tool for managing account access security. Monitoring security parameters may provide visibility, but it does not directly restrict access.

C. A short session timeout can reduce risk from idle sessions, but making it “very short” can harm usability and is not as direct as network restrictions.

D. Every user already has access to the PUBLIC role by default. Setting restrictive permissions on PUBLIC is good practice, but it is not as direct as restricting access through a network policy.

Official Snowflake documentation reference:

Snowflake documentation describes network policies as security controls that restrict access to Snowflake based on allowed and blocked IP address lists.

The correct answer is D. ACCOUNT_USAGE.TABLE_STORAGE_METRICS .

ACCOUNT_USAGE.TABLE_STORAGE_METRICS provides detailed storage metrics for tables, including active bytes, Time Travel bytes, Fail-safe bytes, and retained-for-clone bytes. This view is more detailed for table-level storage than SHOW TABLES or DESC TABLE.

Why D is correct:

TABLE_STORAGE_METRICS is designed specifically to report table-level storage usage.

Why the other options are incorrect:

A. SHOW TABLES provides table metadata, but not the most detailed storage breakdown.

B. DESC TABLE describes the table structure, such as columns and data types, not detailed storage consumption.

C. ACCOUNT_USAGE.STORAGE_USAGE provides account-level storage usage, not the most detailed table-level storage metrics.

Official Snowflake documentation reference:

Snowflake documentation describes ACCOUNT_USAGE.TABLE_STORAGE_METRICS as a view that provides storage information for tables, including active, Time Travel, Fail-safe, and retained-for-clone storage.

The correct answer is C. GET_PATH .

GET_PATH extracts a value from semi-structured data using a path expression. It can be used to access values inside an object, including nested values.

Why C is correct:

For an object stored in a VARIANT or OBJECT column, GET_PATH can retrieve a value by specifying the key path.

Example:

SELECT GET_PATH(src, ' customer.name ' )

FROM my_table;

This accesses the name value inside the customer object.

Why the other options are incorrect:

A. OBJECT_KEYS returns the keys from an object, not the values for a specific key path.

B. OBJECT_PICK returns a new object containing selected key-value pairs, but it is not the general function for accessing a value by path.

D. XMLGET is used for XML data, not general JSON-style object access.

Official Snowflake documentation reference:

Snowflake documentation describes GET_PATH as a function that extracts a value from semi-structured data using a path name.

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.

A.Setting up a multi-cluster virtual warehouseincreases parallelism and throughput but does not directly lower credit consumption. It is more about performance scaling than cost efficiency.

B.Resizing the virtual warehouse to a larger sizeincreases the compute resources available for processing queries, which increases the credit consumption rate. This option does not help in lowering costs.

C.Automating the virtual warehouse suspension and resumption settings: This is a direct method to manage credit consumption efficiently. By automatically suspending a warehouse when it is not in use and resuming it when needed, you can avoid consuming credits during periods of inactivity. Snowflake allows warehouses to be configured to automatically suspend after a specified period of inactivity and to automatically resume when a query is submitted that requires the warehouse.

D.Increasing the maximum cluster count parameter for a multi-cluster virtual warehousewould potentially increase credit consumption by allowing more clusters to run simultaneously. It is used to scale up resources for performance, not to reduce costs.

Automating the operational times of virtual warehouses ensures that you only consume compute credits when the warehouse is actively being used for queries, thereby optimizing your Snowflake credit usage.

The type of Snowflake table that persists until it is explicitly dropped, is available for all users with relevant privileges across sessions, and does not have a Fail-safe period, is a Transient table. Transient tables are designed to provide temporary storage similar to permanent tables but with some reduced storage costs and without the Fail-safe feature, which provides additional data protection for a period beyond the retention time. Transient tables are useful in scenarios where data needs to be temporarily stored for longer than a session but does not require the robust durability guarantees of permanent tables.

The correct answer is B. Hybrid .

A hybrid table is Snowflake’s table type designed for transactional workloads. Hybrid tables support row-level operations and are optimized for low-latency, high-concurrency transactional access patterns.

Why B is correct:

Hybrid tables are intended for transactional and operational workloads, such as applications that require fast point lookups, inserts, updates, and deletes.

Why the other options are incorrect:

A. Event tables store event and log data generated by Snowflake features such as Snowpark logging and tracing.

C. Dynamic tables are used for automated data transformation pipelines. They are not optimized for transactional workloads.

D. Permanent tables are the standard Snowflake table type for persistent analytical storage, but they are not specifically optimized for transactional workloads like hybrid tables.

Official Snowflake documentation reference:

Snowflake documentation describes hybrid tables as Snowflake tables optimized for transactional workloads that require low-latency reads and writes.

The role that can by default create resource monitors in Snowflake is theACCOUNTADMINrole. Resource monitors are a crucial feature in Snowflake that allows administrators to track and control the consumption of compute resources, ensuring that usage stays within specified limits. The creation and management of resource monitors involve defining thresholds for credits usage, setting up notifications, and specifying actions to be taken when certain thresholds are exceeded.

Given the significant impact that resource monitors can have on the operational aspects and billing of a Snowflake account, the capability to create and manage them is restricted to theACCOUNTADMINrole. This role has the broadest set of privileges in Snowflake, including the ability to manage all aspects of the account, such as users, roles, warehouses, databases, and resource monitors, among others.

The correct answer is C. Only the schema owner or roles with the MANAGE GRANTS privilege can grant privileges on objects .

In a managed access schema , privilege management is centralized. Individual object owners do not manage grants on their own objects. Instead, grants on objects inside the schema are managed by the schema owner or a role with the global MANAGE GRANTS privilege.

Why C is correct:

Managed access schemas are designed to simplify and centralize access control. Even if a role owns a table, view, or other object inside the managed access schema, that role cannot independently grant privileges on that object unless it is also the schema owner or has MANAGE GRANTS.

Why the other options are incorrect:

A. This describes behavior in regular schemas, not managed access schemas.

B. Object ownership alone is not enough to manage future grants independently in a managed access schema.

D. The global MANAGE GRANTS privilege is applicable and allows privilege management.

Official Snowflake documentation reference:

Snowflake documentation explains that in managed access schemas, object owners lose the ability to make grant decisions. Only the schema owner or a role with the MANAGE GRANTS privilege can grant privileges on objects in the schema.