COF-C02 SnowPro Core Certification Exam Questions and Answers

The STRIP_NULL_VALUE function in Snowflake is used to convert a JSON null value into a SQL NULL value1.

Views can be used with Secure Data Sharing in Snowflake. Materialized views, external tables, and UDFs are not typically shared directly for security and performance reasons2.

 Once an internal stage is dropped in Snowflake, it cannot be recovered or restored using Time Travel or UNDROP commands. The only option is to recreate the dropped stage

In Snowflake, permanent tables and external tables can be shared with other accounts using Secure Data Sharing. Temporary tables, transient tables, and UDTFs are not shareable objects

To determine which user has accessed a database object containing sensitive information, a Snowflake administrator can query the ACCESS_HISTORY view in the ACCOUNT_USAGE schema, which provides information about access to database objects3.

The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT. These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures

 In Snowflake, a user with the SECURITYADMIN role or higher can activate and enforce a network policy for all users in an account. Additionally, a role that has ownership of the network policy can also activate and enforce it

The COPY INTO <table> command in Snowflake supports column reordering and column omission as part of its data transformation capabilities during the load process45.

The purpose of a Query Profile is to provide a detailed analysis of a particular query’s execution plan, including the mechanics, behavior, and performance. It helps in identifying potential performance bottlenecks and areas for optimization

The Query Profile in Snowflake can identify common query problems, including:

B. Inefficient pruning: This refers to the inability of a query to effectively limit the amount of data being scanned, potentially leading to suboptimal performance.

D. Queries too large to fit in memory: This indicates that a query requires more memory than is available in the virtual warehouse, which can lead to spilling to disk and degraded performance.

The Query Profile helps diagnose these issues by providing detailed execution statistics and visualizations, aiding in query optimization and troubleshooting.

References:

Snowflake Documentation: Query Profile

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Valid sub-clauses to the OVER clause 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

A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAY https://docs.snowflake.com/en/user-guide/tables-clustering-keys

The FLATTEN function can be used with the recursive argument to return a list of distinct key names in all nested elements within an object. This function is particularly useful for working with semi-structured data in Snowflake

The RETURN_ERRORS value in the VALIDATION_MODE option of the COPY command instructs Snowflake to validate the data files and return errors encountered across all specified files, including those that were partially loaded during an earlier load2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The BUILD_STAGE_FILE_URL function generates a Snowflake-hosted file URL to a staged file using the stage name and relative file path as inputs2.

 To validate data unloaded using the COPY INTO command, a Snowflake user can use the VALIDATION_MODE parameter within the SQL statement to test the files for errors without loading them3.

 The COPY INTO  command is used to unload data from a Snowflake database table into one or more files in a Snowflake stage1.

 One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query. This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

In owner’s rights stored procedures, certain commands are restricted to maintain security and integrity. The SHOW and DESCRIBE commands are limited because they can reveal metadata and structure information that may not be intended for all roles. 

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.

The Query Profile statistics that indicate intermediate results do not fit in memory are the bytes spilled to local storage and bytes spilled to remote storage2.

SnowCD is a connectivity diagnostic tool that helps users troubleshoot network connections to Snowflake. It performs a series of checks to evaluate the network connection and provides suggestions for resolving any issues4.

 In a managed access schema, the schema owner retains the OWNERSHIP privilege of any tables added to the schema. This means that while object owners have certain privileges over the objects they create, only the schema owner can manage privilege grants on these objects1.

 A network policy in Snowflake is used to restrict access to the Snowflake account from unauthorized or unknown sources. It allows administrators to specify allowed IP address ranges, thus preventing access from any IP addresses not listed in the policy1.

The DATA_SHARING_USAGE schema includes views that display information about listings published in the Snowflake Marketplace or a data exchange, which includes DATA_TRANSFER_HISTORY and LISTING_TELEMETRY_DAILY2.

 The factors that impact storage costs in Snowflake include the account type (Capacity or On Demand) and the cloud region used by the account. These factors determine the rate at which storage is billed, with different regions potentially having different rates3.

Temporary tables do not consume any storage costs in Snowflake because they only exist for the duration of the session that created them and are automatically dropped when the session ends, thus incurring no long-term storage charges4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The purpose of the Snowflake SPLIT_TO_TABLE function is to split a string based on a specified delimiter and flatten the results into rows. This table function is useful for transforming a delimited string into a set of rows that can be further processed or queried5.

Running a virtual warehouse is required when loading data into a table and when unloading data from a table because these operations require compute resources that are provided by the virtual warehouse23.

In the Snowflake Web Interface (UI), the Query History page displays the history of queries executed in Snowflake for up to 14 days. This allows users to review and analyze their query performance, troubleshoot issues, and understand their query patterns over a two-week period. The Query History page is a critical tool for monitoring and optimizing the use of Snowflake.

References:

Snowflake Documentation: Using the Web Interface

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.

Characteristics of Snowsight worksheets in Snowflake include:

A. Worksheets can be grouped under folders, and a folder of folders: This organizational feature allows users to efficiently manage and categorize their worksheets within Snowsight, Snowflake's web-based UI, enhancing the user experience by keeping related worksheets together.

E. Users can import worksheets and share them with other users: Snowsight supports the sharing of worksheets among users, fostering collaboration by allowing users to share queries, analyses, and findings. This feature is crucial for collaborative data exploration and analysis workflows.

References:

Snowflake Documentation: Snowsight (UI for Snowflake)

The LATERAL modifier for the FLATTEN function allows joining information outside the object (such as other columns in the source table) with the flattened data, creating a lateral view that correlates with the preceding tables in the FROM clause2345. References: [COF-C02] SnowPro Core Certification Exam Study Guide

In Snowflake, overloading refers to the creation of multiple User-Defined Functions (UDFs) with the same name but differing in the number or types of their arguments. This feature allows for more flexible function usage, as Snowflake can differentiate between functions based on the context of their invocation, such as the types or the number of arguments passed. Overloading helps to create more adaptable and readable code, as the same function name can be used for similar operations on different types of data.

References:

Snowflake Documentation: User-Defined Functions

Query pruning in Snowflake is the mechanism used to limit the number of micro-partitions scanned by a query. By analyzing the filters and conditions applied in a query, Snowflake can skip over micro-partitions that do not contain relevant data, thereby reducing the amount of data processed and improving query performance. This technique is particularly effective for large datasets and is a key component of Snowflake's performance optimization features.

References:

Snowflake Documentation: Query Performance Optimization

In Snowflake, stored procedures and User-Defined Functions (UDFs) have different invocation patterns within SQL:

Option B is correct: A single executable statement can call only one stored procedure due to the procedural and potentially transactional nature of stored procedures. In contrast, a single SQL statement can call multiple UDFs because UDFs are designed to operate more like functions in traditional programming, where they return a value and can be embedded within SQL queries.References: Snowflake documentation comparing the operational differences between stored procedures and UDFs.

When a Snowflake user increases the size of a suspended virtual warehouse, the changes to compute resources are queued but do not take immediate effect. The provisioning of additional compute resources occurs only when the warehouse is resumed. This ensures that resources are allocated efficiently, aligning with Snowflake's commitment to cost-effective and on-demand scalability.

References:

Snowflake Documentation: Virtual Warehouses

When unloading data, the Parquet file format is known for its efficiency in preserving the data values for floating-point number columns. Parquet is a columnar storage file format that offers high compression ratios and efficient data encoding schemes. It is especially effective for floating-point data, as it maintains high precision and supports efficient querying and analysis.

References:

Snowflake Documentation: Using the Parquet File Format for Unloading Data

Object Name Resolution: When unqualified object names (e.g., table name without schema) are used in a UDF, Snowflake follows a specific hierarchy to resolve them. Here's the order:

Current Schema: Snowflake first checks if an object with the given name exists in the schema currently in use for the session.

PUBLIC Schema: If the object isn't found in the current schema, Snowflake looks in the PUBLIC schema of the current database.

Note: The SEARCH_PATH parameter influences object resolution for queries, not within UDFs.

References:

Snowflake Documentation (Object Naming Resolution): https://docs.snowflake.com/en/sql-reference/name-resolution.html

A cached MFA token is valid for up to four hours. https://docs.snowflake.com/en/user-guide/security-mfa#using-mfa-token-caching-to-minimize-the-number-of-prompts-during-authentication-optional

The VALIDATE table function in Snowflake would typically use a unique identifier, such as a UUID_STRING, as an input argument. This function is designed to validate the data within a table against a set of constraints or conditions, often requiring a specific identifier to reference the particular data or job being validated.

References:

There is no direct reference to a VALIDATE table function with these specific parameters in Snowflake documentation. It seems like a theoretical example for understanding function arguments. Snowflake documentation on UDFs and system functions can provide guidance on how to create and use custom functions for similar purposes.

A Pre-signed URL provides access to files stored in Snowflake without requiring authorization at the time of access. This feature allows users to generate a URL with a limited validity period that grants temporary access to a file in a secure manner. It's particularly useful for sharing data with external parties or applications without the need for them to authenticate directly with Snowflake.

References:

Snowflake Documentation: Using Pre-signed URLs

The Fail-safe period for a transient table in Snowflake, regardless of the edition (including Enterprise edition and higher), is 0 days. Fail-safe is a data protection feature that provides additional retention beyond the Time Travel period for recovering data in case of accidental deletion or corruption. However, transient tables are designed for temporary or short-term use and do not benefit from the Fail-safe feature, meaning that once their Time Travel period expires, data cannot be recovered.

References:

Snowflake Documentation: Understanding Fail-safe

In Snowflake, when setting up a network policy that specifies both allowed and blocked IP address lists, if an IP address appears in both lists, access from that IP address will be denied. The reason is that Snowflake prioritizes security, and the presence of an IP address in the blocked list indicates it should not be allowed regardless of its presence in the allowed list. This ensures that access controls remain stringent and that any potentially unsafe IP addresses are not inadvertently permitted access.

References:

Snowflake Documentation: Network Policies

To temporarily bypass a network policy by configuring the user object property MINS_TO_BYPASS_NETWORK_POLICY, the USERADMIN role should be used. This role has the necessary privileges to modify user properties, including setting a temporary bypass for network policies, which can be crucial for enabling access under specific circumstances without permanently altering the network security configuration.

References:

Snowflake Documentation: User Management

To verify the privileges that have been granted to a specific role in Snowflake, the correct SQL command is SHOW GRANTS TO ROLE <Role Name>. This command lists all the privileges granted to the specified role, including access to schemas, tables, and other database objects. This is a useful command for administrators and users with sufficient privileges to audit and manage role permissions within the Snowflake environment.

References:

Snowflake Documentation: SHOW GRANTS

UTF-8 is the only supported character set for loading and unloading data from all supported file formats in Snowflake. UTF-8 is a widely used encoding that supports a large range of characters from various languages, making it suitable for internationalization and ensuring data compatibility across different systems and platforms.

References:

Snowflake Documentation: Data Loading and Unloading

By default, the standard retention period for Time Travel in Snowflake is 1 day across all Snowflake accounts. Time Travel enables users to access historical data within this retention window, allowing for point-in-time data analysis and recovery. This feature is a significant aspect of Snowflake's data management capabilities, offering flexibility in handling data changes and accidental deletions.

References:

Snowflake Documentation: Using Time Travel

When the data retention period is changed at the schema level, all child objects that do not have an explicit retention period set will inherit the new retention period from the schema4.

When dealing with semi-structured data in Snowflake, and the specific types of operations to be performed on the data are not yet determined, Snowflake recommends using the VARIANT data type. The VARIANT type is highly flexible and capable of storing data in multiple formats, including JSON, AVRO, BSON, and more, within a single column. This flexibility allows users to perform various operations on the data, including querying and manipulation of nested data structures without predefined schemas.

References:

Snowflake Documentation: Semi-structured Data Types

When querying semi-structured data in Snowflake, the behavior towards case sensitivity is distinct between column names and the names of elements within the semi-structured data. Column names follow the general SQL norm of being case-insensitive, meaning you can reference them in any case without affecting the query. However, element names within JSON, XML, or other semi-structured data are case-sensitive. This distinction is crucial for accurate data retrieval and manipulation in Snowflake, especially when working with JSON objects where the case of keys can significantly alter the outcome of queries.

References:

Snowflake Documentation: Querying Semi-structured Data

The Virtual Private Snowflake (VPS) edition offers the highest level of security for organizations with the strictest security requirements. This edition provides a dedicated and isolated instance of Snowflake, including enhanced security features and compliance certifications to meet the needs of highly regulated industries or any organization requiring the utmost in data protection and privacy.

References:

Snowflake Documentation: Snowflake Editions

By default, the ACCOUNTADMIN role in Snowflake has access to the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function. This function is used to set global account parameters, impacting the entire Snowflake account's configuration and behavior. The ACCOUNTADMIN role is the highest-level administrative role in Snowflake, granting the necessary privileges to manage account settings and security features, including the use of global account parameters.

References:

Snowflake Documentation: SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER

The replication feature in Snowflake provides several benefits, with disaster recovery and database failover and fallback being two of the primary advantages. Replication allows for the continuous copying of data from one Snowflake account to another, ensuring that a secondary copy of the data is available in case of outages or disasters. This capability supports disaster recovery strategies by allowing operations to quickly switch to the replicated data in a different account or region. Additionally, it facilitates database failover and fallback procedures, ensuring business continuity and minimizing downtime.

References:

Snowflake Documentation: Data Replication

For directory tables, a named external stage allows for the automatic refreshing of metadata. This capability is particularly useful when dealing with files stored on external storage services (like Amazon S3, Google Cloud Storage, or Azure Blob Storage) and accessed through Snowflake. The external stage references these files, and the directory table's metadata can be automatically updated to reflect changes in the underlying files.

References:

Snowflake Documentation: External Stages

When data is loaded into Snowflake, it undergoes a reorganization process where the data is stored in a columnar format and compressed. The columnar storage format enables efficient querying and data retrieval, as it allows for reading only the necessary columns for a query, thereby reducing IO operations. Additionally, Snowflake uses advanced compression techniques to minimize storage costs and improve performance. This combination of columnar storage and compression is key to Snowflake's data warehousing capabilities.

References:

Snowflake Documentation: Data Storage and Organization

The search optimization service in Snowflake supports use cases involving conjunctions (AND) of multiple equality predicates. This service enhances the performance of queries that include multiple equality conditions by utilizing search indexes to quickly filter data without scanning entire tables or partitions. It's particularly beneficial for improving the response times of complex queries that rely on specific data matching across multiple conditions.

References:

Snowflake Documentation: Search Optimization Service

The ORGADMIN role in Snowflake is an organizational-level role that provides administrative capabilities across the entire organization, rather than being limited to a single Snowflake account. Users with this role can:

A. Create an account for an organization: The ORGADMIN role has the privilege to create new Snowflake accounts within the organization, allowing for the expansion and management of the organization's resources.

D. View usage information for all accounts in an organization: This role also has access to comprehensive usage and activity data across all accounts within the organization. This is crucial for monitoring, cost management, and optimization at the organizational level.

References:

Snowflake Documentation: Understanding Role-Based Access Control

The Search Optimization Service in Snowflake is designed to improve the performance of specific types of queries, particularly those involving extensive sets of WHERE conditions. By maintaining a search index on tables, this service can accelerate lookup and analytical queries, making it a valuable feature for optimizing query performance and reducing execution times for complex searches.

References:

Snowflake Documentation: Search Optimization Service

To utilize the search optimization service in Snowflake, the correct syntax for granting privileges to a role involves specific commands that include adding search optimization capabilities:

Option C: GRANT ADD SEARCH OPTIMIZATION ON SCHEMA TO ROLE . This command grants the specified role the ability to implement search optimization at the schema level, which is essential for enhancing search capabilities within that schema.

Options A and B do not include the correct verb "ADD," which is necessary for this specific type of grant command in Snowflake. Option D incorrectly mentions the database level, as search optimization privileges are typically configured at the schema level, not the database level.References: Snowflake documentation on the use of GRANT statements for configuring search optimization.

Snowflake supports a variety of file formats for unloading semi-structured data, among which Parquet and Newline Delimited JSON (NDJSON) are two widely used formats.

B. Binary file in Parquet: Parquet is a columnar storage file format optimized for large-scale data processing and analysis. It is especially suited for complex nested data structures.

D. Newline Delimited JSON (NDJSON): This format represents JSON records separated by newline characters, facilitating the storage and processing of multiple, separate JSON objects in a single file.

These formats are chosen for their efficiency and compatibility with data analytics tools and ecosystems, enabling seamless integration and processing of exported data.

References:

Snowflake Documentation: Data Unloading

Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:

Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.

Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.

Options B, D, and E do not directly relate to observing the effects of query pruning. "Partitions total" shows the total available, not the impact of pruning, while "Bytes read from result" and "Bytes written" relate to output rather than the efficiency of data scanning.References: Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.

To execute a series of SQL statements using a task, a user would use a stored procedure that contains multiple SQL statements and invoke this stored procedure from the task. References: Snowflake Documentation2.

Snowflake bills virtual warehouse usage in one-minute increments, rounding up to the nearest minute for any partial minute of compute time used. If a virtual warehouse runs for 61 seconds and then, after being shut down, restarts and runs for an additional 30 seconds, the total time billed would be 120 seconds or 2 minutes. The first 61 seconds are rounded up to 2 minutes, and the subsequent 30 seconds are within a new minute, which is also rounded up to the nearest minute.

References:

Snowflake Documentation: Virtual Warehouses Billing

 The command used to unload files from an internal or external stage to a local file system in Snowflake is the GET command. This command allows users to download data files that have been staged, making them available on the local file system for further use23.

To run a multi-cluster warehouse in maximized mode, a user should set the minimum and maximum number of clusters to the same value. This ensures that all clusters are available when the warehouse is started, providing maximum resources for query execution. References: Snowflake Documentation2.

 For the ALLOWED VALUES tag property, the maximum number of possible string values for a single tag is 256. This allows for a wide range of values to be assigned to a tag when it is set on an object

The parameter that prevents streams on tables from becoming stale is MAXDATAEXTENSIONTIMEINDAYS. This parameter specifies the maximum number of days for which Snowflake can extend the data retention period for the table to prevent streams on the table from becoming stale4.

 The REST API used with unstructured data in Snowflake is GET /api/files/, which retrieves (downloads) a data file from an internal or external stage4.

 When deciding on a clustering key for a table, Snowflake recommends using the columns that are most actively used in the select filters. This is because clustering by these columns can improve the performance of queries that filter on these values, leading to more efficient scans and better overall query performance2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowflake’s column level security features include Dynamic Data Masking and External Tokenization. Dynamic Data Masking uses masking policies to selectively mask data at query time, while External Tokenization allows for the tokenization of data before loading it into Snowflake and detokenizing it at query runtime5.

 In the Activity section in Snowsight, Snowflake users can explore the performance of executed queries. This includes monitoring queries, viewing details about queries, including performance data, and exploring each step of an executed query in the query profile1.

 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 an additional 30 seconds, it is billed for another 60 seconds (the minimum charge for a restart), totaling 121 seconds2

The user property ‘MINS TO BYPASS MFA’ in Snowflake allows temporary bypass of MFA for a user, which can be set by an account administrator without contacting Snowflake Support2.

The Kafka connector stores formatted information in a single column of type VARIANT. This column type is used to store semi-structured data like JSON or Avro, which allows for flexibility in the data structure

Disabling auto-suspend for a virtual warehouse is recommended when there is a steady workload, which ensures that compute resources are always available. Additionally, it is advisable to disable auto-suspend when immediate availability of compute resources is critical, eliminating any startup delay

 Snowflake maintains a result cache that stores the results of every query for 24 hours. If the same query is executed again within this time frame and the data has not changed, Snowflake will retrieve the data from the result cache instead of using a virtual warehouse to recompute the results2.

The details section of a Query Profile in Snowflake provides users with various statistics and information about the execution of a query. One of the key pieces of information that can be determined from this section is the total duration of the query, which helps in understanding the performance and identifying potential bottlenecks. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Parquet file format is known for preserving the precision of floating-point numbers when data is unloaded, preventing truncation of the values3.

 The OBJECT_CONSTRUCT function is used in Snowflake to create a JSON object from relational data, which is useful when unloading semi-structured data. The SINGLE = TRUE parameter is used when unloading data to ensure that the data is exported as a single file, which can be up to 5 GB in size. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Pre-signed URLs in Snowflake allow users to access unstructured data without the need for authentication into Snowflake or passing an authorization token. These URLs are open and can be directly accessed or downloaded by any user or application, making them ideal for business intelligence applications or reporting tools that need to display unstructured file contents

A scoped URL expires when the persisted query result period ends, which is typically after the results cache expires. This is currently set to 24 hours

 An outer join, specifically a left outer join, will list all rows from the left table and match them with rows from the right table. If there is no match, the result will still include the row from the left table, with NULLs for columns from the right table. References: Based on general SQL knowledge as of 2021.

The bulk data load history available after a COPY statement is stored in the metadata of the pipe and is retained for 64 days1.

 Stored procedures in Snowflake can perform a variety of database operations, including DDL and DML, whereas UDFs are designed to return values and cannot execute database operations1.

 In Snowflake, the OPERATE privilege is required for a role to suspend or resume a task. This privilege allows the role to perform operational tasks such as starting and stopping tasks, which includes suspending and resuming them6

To access a JSON object in Snowflake, dot notation is used where the path to the object is specified after the column name containing the JSON data. Both lowercase and uppercase can be used for attribute names, so both “name” and “Name” are valid. References: [COF-C02] SnowPro Core Certification Exam Study Guide

To provide a user with the necessary access to create a materialized view in a schema, the user must be granted a role that has the CREATE MATERIALIZED VIEW privilege on that schema. First, the role is granted to the user, and then the privilege is granted to the role

 In the Information Schema and Account Usage, the column that identifies whether a view is secure or not is IS_SECURE2.

 Running EXPLAIN and SHOW commands, as well as running a custom query, consume virtual warehouse credits in the Snowflake environment. These activities require computational resources, and therefore, credits are used to account for the usage of these resources. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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.

The search optimization service and automatic clustering are features that can improve the performance of queries returning a small subset of rows from a large table. The search optimization service is designed for low-latency point lookup queries, while automatic clustering organizes data in micro-partitions based on specific dimensions to reduce the amount of data scanned during queries.

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

 The stream type that can be used for tracking the records in external tables is ‘External’. This type of stream is specifically designed to track changes in external tables

 In Snowflake, a User-Defined Table Function (UDTF) can be accessed in the FROM clause of a query. UDTFs return a set of rows with one or more columns, which can be queried like a regular table

 In Snowflake, schema objects include Pipes and File formats. Pipes are used for continuous data loading, and File formats specify the format of data files used in loading and unloading operations within Snowflake

 Once a database has been created in a consumer account from a share, the shared objects become accessible to users in that account. The shared objects are not transferred or copied; they remain in the provider’s account and are accessible to the consumer account

The Snowpark API allows developers to create User-Defined Functions (UDFs) in various languages, including Python, which is known for its ease of use and wide adoption in data-related tasks. References: Based on general programming and cloud data service knowledge as of 2021.

The query results cache can be used as long as the data in the table has not changed since the last time the query was run. If the underlying data has changed, Snowflake will not use the cached results and will re-execute the query1.

 Scaling out in Snowflake involves adding clusters of the same size to a virtual warehouse, which allows for handling more concurrent queries without affecting the performance of individual queries. Scaling up refers to resizing a virtual warehouse to increase its compute resources, enabling it to handle more complex workloads and larger queries more efficiently.

The minimum Snowflake edition required to use Dynamic Data Masking is the Enterprise edition. This feature is not available in the Standard edition2.

 Dynamic data masking is a feature in Snowflake that allows administrators to define masking policies to protect sensitive data. It enables partial visibility of the data to certain roles, such as functional managers, while hiding it from others, like end users

A multi-cluster warehouse should be used in auto-scaling mode when there is a need to handle a large number of concurrent queries. Auto-scaling allows Snowflake to automatically add or remove compute clusters to balance the load, ensuring that performance remains consistent during varying levels of demand

 Snowflake’s architecture is described as a multi-cluster, shared data architecture. This design combines the simplicity of a shared-disk architecture with the performance and scale-out benefits of a shared-nothing architecture, using a central repository accessible from all compute nodes2.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

 The COPY INTO SQL command with the option SINGLE=FALSE is used to unload data into multiple files. This option allows the data to be split into multiple files during the unload process. References: SnowPro Core Certification COPY INTO SQL command unload multiple files

 Organizing files into logical paths can significantly improve the efficiency of data loading from an external stage. This practice helps in managing and locating files easily, which can be particularly beneficial when dealing with large datasets or complex directory structures1.

The maximum Time Travel retention period for a temporary Snowflake table is 1 day. This is the standard retention period for temporary tables, which allows for accessing historical data within a 24-hour window

 In Snowflake, each size increase in virtual warehouses doubles the number of servers. Therefore, if a size Small virtual warehouse is made up of two servers, a Large warehouse, which is two sizes larger, would be made up of eight servers (2 servers for Small, 4 for Medium, and 8 for Large)2.

Size specifies the amount of compute resources available per cluster in a warehouse. Snowflake supports the following warehouse sizes:

https://docs.snowflake.com/en/user-guide/warehouses-overview.html

 Snowflake’s Fail-safe provides a 7-day period during which data in a permanent table may be recoverable, but only by Snowflake Support, not by user operations3.

 The COPY command is used in Snowflake to load data from files located in an external stage into a table. This command allows for efficient and parallelized data loading from various file formats1.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation

 The billable compute time includes the time the warehouse is running queries plus the auto-suspend time after the last query if the warehouse is not manually suspended. In this scenario, the warehouse runs for 3 minutes, suspends after 10 minutes of inactivity, resumes for a 10-second query, and then is manually suspended. The total billable time is the sum of the initial 3 minutes, the 10 minutes of auto-suspend time, and the brief period for the 10-second query, which is rounded up to the next full minute due to Snowflake’s billing practices. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Query Profile in Snowflake is used to identify performance issues with queries. Common issues that can be found using the Query Profile include identifying inefficient micro-partition pruning (D) and data spilling to a local or remote disk (E). Micro-partition pruning is related to the efficiency of query execution, and data spilling occurs when the memory is insufficient, causing the query to write data to disk, which can slow down the query performance1.

 Snowflake uses a Role-Based Access Control (RBAC) model to manage access to data and resources. Additionally, Snowflake ensures that all data is encrypted, both at rest and in transit, to provide a high level of security for data stored within the platform. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The Enterprise edition of Snowflake allows for a dedicated metadata store, providing additional features designed for large-scale enterprises

Snowflake supports the use of external stages with Amazon Web Services (AWS), Microsoft Azure Cloud, and Google Cloud Platform (GCP). These platforms allow users to stage data externally and integrate with Snowflake for data loading operations

Snowpipe via REST API can reference both named internal stages within Snowflake and external stages, such as Amazon S3, Google Cloud Storage, or Microsoft Azure1. This means that Snowpipe is not limited to only external stages as a source for data loading.

References = [COF-C02] SnowPro Core Certification Exam Study Guide, Snowflake Documentation1

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

Worksheets in Snowsight can be shared directly with other Snowflake users within the same account. This feature allows for collaboration and sharing of SQL queries or Python code, as well as other data manipulation tasks1.

Snowflake’s data caching features include the ability to clear the least-recently used data when the data cache is full to make room for new data. Additionally, the RESULT_SCAN table function can access and filter the contents of the query result cache, allowing users to retrieve and work with the results of previous queries. The other statements are incorrect: the data cache is not saved on the remote storage layer when a virtual warehouse is suspended, users can access queries from the query result cache that were run by other users, and there is no setting called USE_METADATA_CACHE in Snowflake. References: Caching in the Snowflake Cloud Data Platform, Optimizing the warehouse cache

The responsibilities of Snowflake’s Cloud Service layer include authentication (A), which ensures secure access to the platform; resource management (B), which involves allocating and managing compute resources; and query parsing and optimization (D), which improves the efficiency and performance of SQL query execution3.

 Snowpipe retains load history for 14 days. This allows users to view and audit the data that has been loaded into Snowflake using Snowpipe within this time frame3.

The UNDROP command in Snowflake can be used to directly restore Views and Tables. These objects, when dropped, are moved to a ‘Recycle Bin’ where they can be restored within a time limit before they are permanently deleted. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 When the retention period for an object ends in Snowflake, Time Travel on the historical data is dropped ©. This means that the ability to access historical data via Time Travel is no longer available once the retention period has expired2.

 By default, the Snowflake role required to create a share is ACCOUNTADMIN (D). This role has the necessary privileges to perform administrative tasks, including creating shares for data sharing purposes

When publishing a Snowflake Data Marketplace listing into a remote region, it’s important to note that while the listing is replicated into all selected regions automatically, the data itself is not. Therefore, the data must be replicated separately. Additionally, the user must have the ORGADMIN role in at least one account to manage the replication of accounts1.

 In Snowflake’s architecture, the Cloud Services layer is responsible for generating the query execution plan. This layer handles all the coordination, optimization, and management tasks, including query parsing, optimization, and compilation into an execution plan that can be processed by the Compute layer.

An explain plan is beneficial because it allows for the evaluation of how a query will be processed without the need to actually execute the query. This can help in understanding the query’s performance implications and potential bottlenecks without consuming resources that would be used if the query were run

Network policies in Snowflake can be set at the user level and at the account level2.

Snowflake recommends file sizes between 100-250 MB compressed when loading data to optimize parallel processing. Smaller, compressed files can be loaded in parallel, which maximizes the efficiency of the virtual warehouses and speeds up the data loading process

 In an auto-scaling multi-cluster virtual warehouse with the SCALING_POLICY set to ECONOMY, another cluster is started when the system has enough load for 2 minutes (A). This policy is designed to optimize the balance between performance and cost, starting additional clusters only when the sustained load justifies it2.

For data loading using Snowpipe, the recommended file size is a compressed file greater than 10 MB and up to 100 MB. This size range is optimal for Snowpipe’s continuous, micro-batch loading process, allowing for efficient and timely data ingestion without overwhelming the system with files that are too large or too small.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Snowpipe1

The parse_json() function in Snowflake interprets an input string as a JSON document and produces a VARIANT value containing the JSON document. This function is specifically designed for parsing strings that contain valid JSON information3.

 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

The SQL command Select * from table(validate(t1, job_id => '_last')); is used to return errors from the last executed COPY command into table t1 in the current session. It checks the results of the most recent data load operation and provides details on any errors that occurred during that process1.

 Snowflake applies egress charges in the case of database replication when data is transferred out of a Snowflake region to another region or cloud provider. This is because the data transfer incurs costs associated with moving data across different networks. Egress charges are not applied for data sharing within the same region, query result retrieval, or loading data into Snowflake, as these actions do not involve data transfer across regions.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Data Replication and Egress Charges1

When a Snowflake virtual warehouse is resized, the data cached in the warehouse is not lost. This is because the cache is maintained independently of the warehouse size. Resizing a warehouse, whether scaling up or down, does not affect the cached data, ensuring that query performance is not impacted by such changes.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Virtual Warehouse Performance1

The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment. This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.

https://docs.snowflake.com/en/sql-reference/external-functions.html

To return results from the results cache in Snowflake, certain conditions must be met:

Privileges: The user must have the appropriate privileges on the objects associated with the query. This ensures that only authorized users can access cached data.

Time Frame: The query must have been run within 24 hours of the previously-run query. Snowflake’s results cache is designed to store the results of queries for a short period, typically 24 hours, to improve performance for repeated queries.

The Information Schema and Account Usage Share in Snowflake provide metadata and historical usage data for various objects within a Snowflake account. Specifically, they offer storage information for Tables, Databases, and Internal Stages. These schemas contain views and table functions that allow users to query object metadata and usage metrics, such as the amount of data stored and historical activity.

Tables: The storage information includes data on the daily average amount of data in database tables.

Databases: For databases, the storage usage is calculated based on all the data contained within the database, including tables and stages.

Internal Stages: Internal stages are locations within Snowflake for temporarily storing data, and their storage usage is also tracked.

References: The information is verified according to the SnowPro Core Certification Study Guide and Snowflake documentation

The list command in Snowflake is used to validate and display the list of files in a specified stage. When a user has unloaded data to a stage, running the list @file__stage command will show all the files that have been uploaded to that stage, allowing the user to verify the data that was unloaded.

References:

Snowflake Documentation on Stages

SnowPro® Core Certification Study Guide

 In Snowflake, a reader account is a type of account that is intended for consuming shared data rather than performing any data management or DDL operations. The CREATE SHARE command is used to share data from your account with another account, which is not a capability provided to reader accounts. Reader accounts are typically restricted from creating shares, as their primary purpose is to read shared data rather than to share it themselves.

References:

Snowflake Documentation on Reader Accounts

SnowPro® Core Certification Study Guide

 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.

Secure sharing in Snowflake allows users to share specific objects with other Snowflake accounts without physically copying the data, thus not consuming additional storage. Tables and Secure User Defined Functions (UDFs) are among the objects that can be shared using this feature. Materialized views, sequences, and procedures are not shareable objects in Snowflake.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Secure Data Sharing1

 The Snowflake Cloud Services layer manages a variety of services that are crucial for the operation of the Snowflake platform. Among these services, Authentication and Metadata management are key components. Authentication is essential for controlling access to the Snowflake environment, ensuring that only authorized users can perform actions within the platform. Metadata management involves handling all the metadata related to objects within Snowflake, such as tables, views, and databases, which is vital for the organization and retrieval of data.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation12

https://docs.snowflake.com/en/user-guide/intro-key-concepts.html

The ACCOUNTADMIN role is required for a user to create and manage accounts in Snowflake. This role has the highest level of privileges and is responsible for managing all aspects of the Snowflake account, including the ability to create and manage other user accounts1.

https://docs.snowflake.com/en/user-guide/security-access-control-considerations.html

 When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.

On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake’s storage.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Stages

Provisioning time can vary based on the size of the warehouse. A 4X-Large Warehouse typically has more resources and may take longer to provision compared to a X-Small Warehouse, which has fewer resources and can generally be provisioned more quickly.References: Understanding and viewing Fail-safe | Snowflake Documentation

Secure data sharing in Snowflake allows users to share various objects between Snowflake accounts without physically copying the data, thus not consuming additional storage. Among the options provided, external tables can be shared through secure data sharing. External tables are used to query data directly from files in a stage without loading the data into Snowflake tables, making them suitable for sharing across different Snowflake accounts.

References:

Snowflake Documentation on Secure Data Sharing

SnowPro™ Core Certification Companion: Hands-on Preparation and Practice

When loading data into Snowflake, it is recommended to:

C. Load files that are approximately 100-250 MB (or larger): This size is optimal for parallel processing and can help to maximize throughput. Smaller files can lead to overhead that outweighs the actual data processing time.

D. Avoid using embedded characters such as commas for numeric data types: Embedded characters can cause issues during data loading as they may be interpreted incorrectly. It’s best to clean the data of such characters to ensure accurate and efficient data loading.

These best practices are designed to optimize the data loading process, ensuring that data is loaded quickly and accurately into Snowflake.

References:

Snowflake Documentation on Data Loading Considerations

[COF-C02] SnowPro Core Certification Exam Study Guide

Alation is known for specializing in data catalog solutions and is a partner of Snowflake. Data catalog solutions are essential for organizations to effectively manage their metadata and make it easily accessible and understandable for users, which aligns with the capabilities provided by Alation.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s official documentation and partner listings

Data Robot is recognized as a machine learning and data science partner within the Snowflake Partner Ecosystem. It provides an enterprise AI platform that enables users to build and deploy accurate predictive models quickly. As a partner, Data Robot integrates with Snowflake to enhance data science capabilities2.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Machine Learning & Data Science Partners

https://docs.snowflake.com/en/user-guide/ecosystem-analytics.html

 Even if a developer is granted ownership of a table with a masking policy, they will not be able to modify the table to read the masked data if their role does not have the necessary permissions. Ownership of a table does not automatically confer the ability to alter masking policies, which are designed to protect sensitive data. Masking policies are applied at the schema level and require specific privileges to modify12.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Masking Policies

The COPY command in Snowflake allows for the reordering of columns as they are loaded into a table, and it also permits the omission of columns from the source file during the load process. This provides flexibility in handling the schema of the data being ingested. References: [COF-C02] SnowPro Core Certification Exam Study Guide

A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.

The indicators that it may be appropriate to use a clustering key for a table include:

D. Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.

E. The clustering depth for the table is large: A large clustering depth indicates that the table’s data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.

References:

Snowflake Documentation on Clustering Keys & Clustered Tables

Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION

Stack Overflow discussion on cluster key selection in Snowflake

Snowflake’s Time Travel feature is used for both querying historical data in tables and restoring and cloning historical data in databases, schemas, and tables3. It allows users to access historical data within a defined period (1 day by default, up to 90 days for Snowflake Enterprise Edition) and is a key feature for data recovery and management. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Snowflake’s architecture allows for the use of a query result cache that stores the results of queries for a period of time. If the same query is run again and the underlying data has not changed, Snowflake can retrieve the result from this cache without needing to re-run the query on an active warehouse, thus saving on compute resources.

 Snowflake’s architecture is designed to handle high concurrency workloads through several features, two of which are particularly effective:

B. The use of warehouse auto scaling: This feature allows Snowflake to automatically adjust the compute resources allocated to a virtual warehouse in response to the workload. If there is an increase in concurrent queries, Snowflake can scale up the resources to maintain performance.

D. Use of multi-clustered warehouses: Multi-clustered warehouses enable Snowflake to run multiple clusters of compute resources simultaneously. This allows for the distribution of queries across clusters, thereby reducing the load on any single cluster and improving the system’s ability to handle a high number of concurrent queries.

These features ensure that Snowflake can manage varying levels of demand without manual intervention, providing a seamless experience even during peak usage.

References:

Snowflake Documentation on Virtual Warehouses

SnowPro® Core Certification Study Guide

SnowSQL is the command-line client for Snowflake that allows users to execute SQL queries and perform all DDL and DML operations, including staging files for bulk data loading. It is specifically designed for scripting and automating tasks.

References:

SnowPro Core Certification Exam Study Guide

Snowflake Documentation on SnowSQL

https://docs.snowflake.com/en/user-guide/snowsql-use.html

Assigning the custom role to the SYSADMIN role is considered a best practice because it allows the SYSADMIN role to manage objects created by the custom role. This is important for maintaining proper access control and ensuring that the SYSADMIN can perform necessary administrative tasks on objects created by users with the custom role.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Section 1.3 - SnowPro Core Certification Study Guide1

 In all Snowflake editions, two key capabilities are universally available:

B. Automatic encryption of all data: Snowflake automatically encrypts all data stored in its platform, ensuring security and compliance with various regulations. This encryption is transparent to users and does not require any configuration or management.

D. Object-level access control: Snowflake provides granular access control mechanisms that allow administrators to define permissions at the object level, including databases, schemas, tables, and views. This ensures that only authorized users can access specific data objects.

These features are part of Snowflake’s commitment to security and governance, and they are included in every edition of the Snowflake Data Cloud.

References:

Snowflake Documentation on Security Features

SnowPro® Core Certification Exam Study Guide

Transient tables in Snowflake have a default Time Travel retention period of 1 day, which allows users to access historical data within the last 24 hours. However, transient tables do not have a Fail-safe period. Fail-safe is an additional layer of data protection that retains data beyond the Time Travel period for recovery purposes in case of extreme data loss. Since transient tables are designed for temporary or intermediate workloads with no requirement for long-term durability, they do not include a Fail-safe period by default1.

References:

Snowflake Documentation on Storage Costs for Time Travel and Fail-safe

 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 skip over 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-C02] SnowPro Core Certification Exam Study Guide

https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html

 Clustering keys in Snowflake work by sorting the designated columns over time. This process is done in the background and does not block data manipulation language (DML) operations, allowing for normal database operations to continue without interruption. The purpose of clustering keys is to organize the data within micro-partitions to optimize query performance1.

References:

[COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake Documentation on Clustering1

Snowflake does not require source data files to be no larger than 16MB. In fact, Snowflake recommends that for optimal load performance, data files should be roughly 100-250 MB in size when compressed. However, it is not recommended to load very large files (e.g., 100 GB or larger) due to potential delays and wasted credits if errors occur. Smaller files should be aggregated to minimize processing overhead, and larger files should be split to distribute the load among compute resources in an active warehouse.

References: Preparing your data files | Snowflake Documentation

Snowflake’s architecture allows for flexibility in account hosting across different cloud vendors and regions. This means that within a single organization, different Snowflake accounts can be set up in various cloud environments, such as AWS, Azure, or GCP, and in different geographical regions. This allows organizations to leverage the global infrastructure of multiple cloud providers and optimize their data storage and computing needs based on regional requirements, data sovereignty laws, and other considerations.

https://docs.snowflake.com/en/user-guide/intro-regions.html

 Snowflake’s default Multi-Factor Authentication (MFA) service is powered by Duo Security. Users are required to install the Duo Mobile application on their devices to use MFA for increased login security when connecting to Snowflake. This service is managed entirely by Snowflake, and users do not need to sign up separately with Duo1.

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

Tri-secret secure encryption is a security model employed by Snowflake that involves combining a customer-managed key with a Snowflake-managed key to create a composite key for encrypting data. This model enhances data security by requiring both the customer-managed key and the Snowflake-managed key to decrypt data, thus ensuring that neither party can access the data independently. It represents a balanced approach to key management, leveraging both customer control and Snowflake's managed services for robust data encryption.

References:

Snowflake Documentation: Encryption and Key Management

The correct command to view all roles granted to a specific user in Snowflake is SHOW GRANTS TO USER ;. This command lists all access control privileges that have been explicitly granted to the specified user. References: SHOW GRANTS | Snowflake Documentation

The correct function to use with the COPY INTO <location> statement to convert rows from a relational table into a single variant column and to unload rows into a JSON file is TO VARIANT. The TO VARIANT function is used to explicitly convert a value of any supported data type into a VARIANT data type. This is particularly useful when needing to aggregate multiple columns or complex data structures into a single JSON-formatted string, which can then be unloaded into a file.

In the context of unloading data, the COPY INTO <location> statement combined with TO VARIANT enables the conversion of structured data from Snowflake tables into a semi-structured VARIANT format, typically JSON, which can then be efficiently exported and stored. This approach is often utilized for data integration scenarios, backups, or when data needs to be shared in a format that is easily consumed by various applications or services that support JSON.

References:

Snowflake Documentation on Data Unloading: Unloading Data

Snowflake Documentation on VARIANT Data Type: Working with JSON