COF-C02 SnowPro Core Certification Exam Questions and Answers

 In a CREATE PIPE ... AS COPY ... FROM (....) statement, the supported transformations include filtering data using an optional WHERE clause and omitting columns. The WHERE clause allows for the specification of conditions to filter the data that is being loaded, ensuring only relevant data is inserted into the table. Omitting columns enables the exclusion of certain columns from the data load, which can be useful when the incoming data contains more columns than are needed for the target table.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Simple Transformations During a Load1

 Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake’s continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Snowpipe and Virtual Warehouses1

 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

 When unloading data to a stage, it is recommended to define an individual file format. This ensures that the data is unloaded in a consistent and expected format, which can be crucial for downstream processing and analysis2

 Clustering is a technique used in Snowflake to improve the performance of queries. It involves organizing the data in a table into micro-partitions based on the values of one or more columns. This organization allows Snowflake to efficiently prune non-relevant micro-partitions during a query, which reduces the amount of data scanned and improves query performance.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Clustering

 External functions in Snowflake are a special type of User-Defined Function (UDF) that call code executed outside of Snowflake, typically through a remote service. Unlike traditional UDFs, external functions do not contain SQL code within Snowflake; instead, they interact with external services to process data2.

https://docs.snowflake.com/en/sql-reference/external-functions.html#:~:text=External%20functions%20are%20user%2Ddefined,code%20running%20outside%20of%20Snowflake .

Snowpipe data load history is retained for 64 days. This retention period allows users to review and audit the data load operations performed by Snowpipe over a significant period of time, which can be crucial for troubleshooting and ensuring data integrity.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Snowpipe1

In Snowflake, a Pipe is the object designed to enable the continuous, near-real-time loading of data from files as soon as they are available in a cloud storage location. Pipes use Snowflake’s COPY command to load data and can be associated with a Stage object to monitor for new files. When new data files appear in the stage, the pipe automatically loads the data into the target table.

References:

• Snowflake Documentation on Pipes
• SnowPro® Core Certification Study Guide

https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html

The PUT command is used to load data into an internal stage in Snowflake. This command uploads data files from a local file system to a named internal stage, making the data available for subsequent loading into a Snowflake table using the COPY INTO command.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Data Loading

In Snowflake, the COPY INTO <location> command is used to unload (export) data from a Snowflake table to an external stage, such as an S3 bucket, Azure Blob Storage, or Google Cloud Storage. This command allows users to specify the format, file size, and other options for the data being unloaded, making it a flexible solution for exporting data from Snowflake to external storage solutions for further use or analysis.References: Snowflake Documentation on Data Unloading

Snowflake streams are objects that enable users to track changes (inserts, updates, and deletes) to the data in tables, facilitating real-time data processing and integration workflows. Streams can be created on standard tables, capturing DML changes made to these tables so that downstream processes can consume the changes incrementally. This feature supports efficient data ETL, replication, and real-time analytics by providing a mechanism to process only the data that has changed. Note: The correct options should likely include a distinction between "Standard tables" and another object type such as "External tables" rather than repeating "Standard tables" twice.References: Snowflake Documentation on Streams

 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

 Clustering keys in Snowflake are used to reorganize large tables based on one or more columns. This feature optimizes the arrangement of data within micro-partitions to improve query performance, especially for large tables where efficient data retrieval is crucial. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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

A VARIANT column in Snowflake can store semi-structured data types. This includes:

• B. OBJECT: An object is a collection of key-value pairs in JSON, and a VARIANT column can store this type of data structure.
• D. ARRAY: An array is an ordered list of zero or more values, which can be of any variant-supported data type, including objects or other arrays.

The VARIANT data type is specifically designed to handle semi-structured data like JSON, Avro, ORC, Parquet, or XML, allowing for the storage of nested and complex data structures.

References:

• Snowflake Documentation on Semi-Structured Data Types
• SnowPro® Core Certification Study Guide

 When a query in Snowflake is too large to fit into the available memory, it will start spilling to remote storage. This is an indication that the memory allocated for the query is insufficient for its execution, and as a result, Snowflake uses remote disk storage to handle the overflow. This spill to remote storage can lead to slower query performance due to the additional I/O operations required.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Query Profile1
• Snowpro Core Certification Exam Flashcards2

 To address a high volume of queries queuing in a warehouse, Snowflake recommends two best practices:

• A. Use multi-clustered warehousing to scale out warehouse capacity: This approach allows for the distribution of queries across multiple clusters within a warehouse, effectively managing the load and reducing the queue volume.
• B. Scale up the warehouse size to allow Queries to execute faster: Increasing the size of the warehouse provides more compute resources, which can reduce the time it takes for queries to execute and thus decrease the number of queries waiting in the queue.

These strategies help to optimize the performance of the warehouse by ensuring that resources are scaled appropriately to meet demand.

References:

• Snowflake Documentation on Multi-Cluster Warehousing
• SnowPro Core Certification best practices

 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

 The COPY INTO command in Snowflake can be configured to output data into a single file by setting the MAX_FILE_NUMBER option to 1. This option limits the number of files generated by the command, ensuring that only one file is created regardless of the amount of data being exported.

References:

• [COF-C02] SnowPro Core Certification Exam Study Guide
• Snowflake Documentation on Data Unloading

The default file format for the COPY command in Snowflake, when not specified, is CSV (Comma-Separated Values). This format is widely used for data exchange because it is simple, easy to read, and supported by many data analysis tools.

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

Snowflake’s Continuous Data Protection (CDP) encompasses a set of features that help protect data stored in Snowflake against human error, malicious acts, and software failure. Time Travel allows users to access historical data (i.e., data that has been changed or deleted) for a defined period, enabling querying and restoring of data. Fail-safe is an additional layer of data protection that provides a recovery option in the event of significant data loss or corruption, which can only be performed by Snowflake.

References:

• Continuous Data Protection | Snowflake Documentation1
• Data Storage Considerations | Snowflake Documentation2
• Snowflake SnowPro Core Certification Study Guide3
• Snowflake Data Cloud Glossary

https://docs.snowflake.com/en/user-guide/data-availability.html

Fail-safe is a non-configurable feature in Snowflake that provides an additional layer of data protection beyond Time Travel. Time Travel allows users to access historical data within a configurable period (up to 90 days), while Fail-safe provides an additional 7-day period during which Snowflake retains historical data to recover from significant data loss or corruption incidents. This period is not accessible by users but can be used by Snowflake support to assist in data recovery efforts.References: Snowflake Documentation on Fail-safe and Time Travel

Access History in Snowflake is a feature designed to support auditing by tracking access to data within Snowflake, including when a user's query reads column data. It provides detailed information on queries executed, including the user who ran the query, the query text, and the objects (e.g., tables, views) accessed by the query. This feature is instrumental for auditing purposes, helping organizations to monitor and audit data access for security and compliance.

 The size of the virtual warehouse for a task can be configured to handle concurrency automatically using a Multi-cluster warehouse (MCW). This is because tasks are designed to run their body on a schedule, and MCW allows for scaling compute resources to match the task’s execution needs without manual intervention. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake implements Discretionary Access Control (DAC) by using a role-based access control model. In this model, access privileges are not directly assigned to individual objects or users but are encapsulated within roles. These roles are then assigned to users, effectively granting them the access privileges contained within the role. This approach allows for granular control over database access, making it easier to manage permissions in a scalable and flexible manner.References: Snowflake Documentation on Access Control

Temporary and transient tables in Snowflake share several characteristics, notably, neither table type has a Fail-safe period. Fail-safe is a feature that provides additional data protection beyond the Time Travel period. However, this feature does not apply to temporary or transient tables. Additionally, for both types of tables, the data retention period effectively ends when the tables are dropped. This means that once these tables are deleted, their data is not recoverable, distinguishing them from permanent tables, which benefit from Snowflake's Time Travel and Fail-safe features.References: Snowflake Documentation on Table Types

In Snowflake, the use of streams impacts how the data retention period for a table is handled, particularly in scenarios where the stream has not been consumed. The key point to understand is that Snowflake's streams are designed to capture data manipulation language (DML) changes such as INSERTS, UPDATES, and DELETES that occur on a source table. Streams maintain a record of these changes until they are consumed by a DML operation or a COPY command that references the stream.

When a stream is created on a table and remains unconsumed, Snowflake extends the data retention period of the table to ensure that the changes captured by the stream are preserved. This extension is specifically up to the point in time represented by the stream's offset, which effectively ensures that the data necessary for consuming the stream's contents is retained. This mechanism is in place to prevent data loss and ensure the integrity of the stream's data, facilitating accurate and reliable data processing and analysis based on the captured DML changes.

This behavior emphasizes the importance of managing streams and their consumption appropriately to balance between data retention needs and storage costs. It's also crucial to understand how this temporary extension of the data retention period impacts the overall management of data within Snowflake, including aspects related to data lifecycle, storage cost implications, and the planning of data consumption strategies.

References:

• Snowflake Documentation on Streams: Using Streams
• Snowflake Documentation on Data Retention: Understanding Data Retention

While Snowflake primarily deals with structured and semi-structured data, it also has the capability to handle unstructured data. Unstructured data can be stored in Snowflake using variants of SQL data types in tables, which can be permanent tables. These permanent tables, while traditionally used for structured or semi-structured data (like JSON, Avro, or Parquet), can also accommodate unstructured data in the form of binary formats or strings, offering flexibility in data storage and analysis. However, the management and querying of unstructured data in Snowflake may require additional considerations compared to structured data.References: Snowflake Documentation on Data Types

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

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To see the definition of a secure view in Snowflake, the minimum privilege required is OWNERSHIP of the view. Ownership grants the ability to view the definition as well as to modify or drop the view. Secure views are designed to protect sensitive data, and thus the definition of these views is restricted to users with sufficient privileges to ensure data security.

References:

• Snowflake Documentation: Secure Views

To upload data files from a local directory or folder on a client machine to an internal stage in Snowflake, the PUT command is used. The PUT command takes files from the local file system and uploads them to an internal Snowflake stage (or a specified stage) for the purpose of preparing the data to be loaded into Snowflake tables.

Syntax Example:

PUT file:// @;

This command is crucial for data ingestion workflows in Snowflake, especially when preparing to load data using the COPY INTO command.

Snowsight, Snowflake's user interface for exploring, analyzing, and visualizing data, supports a variety of chart types for creating dashboards and visualizations. One of the supported chart types in Snowsight is the Area Chart (A). Area charts are useful for representing quantities over time and can be used to highlight volume change and rate of change, as well as to compare multiple quantities.

While Snowsight supports many types of visualizations to help users analyze their data effectively, including line charts, bar charts, and scatter plots, it's important to select the specific reference documentation or release notes for the most current list of supported chart types, as Snowflake continues to enhance and update Snowsight's capabilities.

As of the last update, Box plots (B), Heat grids (C), and Pie charts (D) are types of visualizations that may be supported in various analytics and visualization tools, but for the specific context of Snowsight's currently confirmed features, Area charts are a verified option for users to visualize their data.

To use the overwrite option on insert in Snowflake, the DELETE privilege must be granted to the role. This is because overwriting data during an insert operation implicitly involves deleting the existing data before inserting the new data.

• Understanding the Overwrite Option: The overwrite option (INSERT OVERWRITE) allows you to replace existing data in a table with new data. This operation is particularly useful for batch-loading scenarios where the entire dataset needs to be refreshed.
• Why DELETE Privilege is Required: Since the overwrite operation involves removing existing rows in the table, the executing role must have the DELETE privilege to carry out both the deletion of old data and the insertion of new data.
• Granting DELETE Privilege:

sqlCopy code

GRANT DELETE ON TABLE my_table TO ROLE my_role;

In Snowflake, the default access level for any securable object (such as a table, view, or schema) is "No access" until explicit access is granted. This means that when an object is created, only the owner of the object and roles with the necessary privileges can access it. Other users or roles will not have any form of access to the object until it is explicitly granted.

This design adheres to the principle of least privilege, ensuring that access to data is tightly controlled and that users and roles only have the access necessary for their functions. To grant access, the owner of the object or a role with the GRANT option can use the GRANT statement to provide specific privileges to other users or roles.

For example, to grant SELECT access on a table to a specific role, you would use a command similar to:

GRANT SELECT ON TABLE my_table TO ROLE my_role;

• Managed Access Schemas: These are a special type of schema designed for fine-grained access control in Snowflake.
• Roles with Grant Authority:
• Important Note: The ORGADMIN role focuses on organization-level management, not object access control.

The command used to export or unload data from Snowflake to a stage (such as a file in an S3 bucket, Azure Blob Storage, or Google Cloud Storage) is the PUT command. The PUT command is designed to upload data files from a local file system (in the case of SnowSQL or other client) or a virtual warehouse to a specified stage. This functionality is critical for scenarios where data needs to be extracted from Snowflake for use in external systems, backups, or further processing.

The syntax for the PUT command follows the structure: PUT file://<local_file_path> @<stage_name>, where <local_file_path> specifies the path to the file(s) on the local file system that you wish to upload, and <stage_name> specifies the destination stage in Snowflake.

It's important to distinguish that the PUT command is used for exporting data out of Snowflake, whereas the COPY INTO <table> command is used for importing data into Snowflake from a stage. The GET command, on the other hand, is used to download files from a stage to the local file system, essentially the inverse operation of the PUT command.

References:

• Snowflake Documentation on Loading and Unloading Data: [Loading and Unloading Data](https://docs.snowflake.com/en/user-guide/data-load

A Reader account in Snowflake can be used to share data with a consumer who does not have a Snowflake account. Reader accounts are a type of shared account provided by data providers to external data consumers, allowing them to access and query shared data using Snowflake's web interface without needing their own Snowflake account.

References:

• Snowflake Documentation: Reader Accounts

In Snowflake, shares are used to share data between Snowflake accounts. When creating a share, it is possible to grant access to the share to roles within the Snowflake account that is creating the share. The type of role that can be granted to a share is a Custom role. Custom roles are user-defined roles that account administrators can create to manage access control in a more granular way. Unlike predefined roles such as ACCOUNTADMIN or SYSADMIN, custom roles can be tailored with specific privileges to meet the security and access requirements of different groups within an organization.

Granting a custom role access to a share enables users associated with that role to access the shared data if the share is received by another Snowflake account. It is important to carefully manage the privileges granted to custom roles to ensure that data sharing aligns with organizational policies and data governance standards.

References:

• Snowflake Documentation on Shares: Shares
• Snowflake Documentation on Roles: Access Control

In Snowflake, the consumption of credits by a virtual warehouse is determined by its size and the duration for which it runs. A size 3X-Large virtual warehouse consumes 128 credits if it runs continuously for 2 hours. This consumption rate is based on the principle that larger warehouses, capable of providing greater computational resources and throughput, consume more credits per hour of operation. The specific rate of consumption is defined by Snowflake’s pricing model and the scale of the virtual warehouse.References: Snowflake Pricing Documentation

The PARTITION BY <expression> parameter option in the COPY INTO <location> command is used to split the output into multiple files based on the distinct values of the specified expression. This feature is particularly useful for organizing large datasets into smaller, more manageable files and can help with optimizing downstream processing or consumption of the data. For example, if you are unloading a large dataset of transactions and use PARTITION BY DATE(transactions.transaction_date), Snowflake generates a separate output file for each unique transaction date, facilitating easier data management and access.

This approach to data unloading can significantly improve efficiency when dealing with large volumes of data by enabling parallel processing and simplifying data retrieval based on specific criteria or dimensions.

References:

• Snowflake Documentation on Unloading Data: COPY INTO

The JAROWINKLER_SIMILARITY function in Snowflake returns an integer between 0 and 100, indicating the similarity of two strings based on the Jaro-Winkler similarity algorithm. This function is useful for comparing strings and determining how closely they match each other.

• Understanding JAROWINKLER_SIMILARITY: The Jaro-Winkler similarity metric is a measure of similarity between two strings. The score is a number between 0 and 100, where 100 indicates an exact match and lower scores indicate less similarity.
• Usage Example: To compare two strings and get their similarity score, you can use:

SELECT JAROWINKLER_SIMILARITY('string1', 'string2') AS similarity_score;

• Application Scenarios: This function is particularly useful in data cleaning, matching, and deduplication tasks where you need to identify similar but not identical strings, such as names, addresses, or product titles.

In Snowflake, a Temporary table is a type of table that is only visible to the user who creates it, can have the same name as permanent tables in the same schema, and is automatically dropped at the end of the session in which it was created. Temporary tables are designed for transient data processing needs, where data is needed for the duration of a specific task or session but not beyond. Since they are automatically cleaned up at the end of the session, they help manage storage usage efficiently and ensure that sensitive data is not inadvertently persisted.

References:

• Snowflake Documentation on Temporary Tables: Temporary Tables

In Snowflake, tag inheritance is a feature that allows tags, which are key-value pairs assigned to objects for the purpose of data governance and metadata management, to be inherited within a hierarchy. The hierarchy in which tag inheritance is possible is from Schema to Table to Column. This means that a tag applied to a schema can be inherited by the tables within that schema, and a tag applied to a table can be inherited by the columns within that table.References: Snowflake Documentation on Tagging and Object Hierarchy

To list all network policies available in an account, the correct command is SHOW NETWORK POLICIES. Network policies in Snowflake are used to define and enforce rules for how users can connect to Snowflake, including IP whitelisting and other connection requirements. The SHOW NETWORK POLICIES command provides a list of all network policies defined within the account, along with their details.

The DESCRIBE SESSION POLICY and DESCRIBE NETWORK POLICY commands do not exist in Snowflake SQL syntax. The SHOW SESSION POLICIES command is also incorrect, as it does not pertain to the correct naming convention used by Snowflake for network policy management.

Using SHOW NETWORK POLICIES without any additional parameters will display all network policies in the account, which is useful for administrators to review and manage the security configurations pertaining to network access.

• Incorrect Commands: The options referencing "RESUME", "SUSPEND", and "ADD" are not valid commands in the context of secondary roles.

References:

• Snowflake Documentation (USE SECONDARY ROLES): https://docs.snowflake.com/en/sql-reference/sql/use-secondary-roles.html

The ACCOUNTADMIN role in Snowflake has the highest level of privileges, including the ability to manage accounts, users, roles, and all objects within the account. This role is specifically granted the permission to rename an account and specify whether the original URL can be used to access the renamed account. The ACCOUNTADMIN role encompasses broad administrative capabilities, ensuring that users assigned this role can perform critical account management tasks.References: Snowflake Documentation on Roles and Permissions

 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

In Snowflake, Secure User-Defined Functions (UDFs) can be shared with other accounts using Snowflake's data sharing feature. This allows different Snowflake accounts to securely execute the UDFs without having direct access to the underlying data the functions operate on, ensuring privacy and security. The sharing is facilitated through shares created in Snowflake, which can contain Secure UDFs along with other database objects like tables and views.References: Snowflake Documentation on Data Sharing and Secure UDFs

Snowflake supports a variety of subquery types, including both correlated and uncorrelated subqueries. The correct answers are B and E, which highlight Snowflake's flexibility in handling subqueries within SQL queries.

• Uncorrelated Scalar Subqueries: These are subqueries that can execute independently of the outer query. They return a single value and can be used anywhere a value expression is allowed, offering great flexibility in SQL queries.
• EXISTS, ANY/ALL, and IN Subqueries: These subqueries are used in WHERE clauses to filter the results of the main query based on the presence or absence of matching rows in a subquery. Snowflake supports both correlated and uncorrelated versions of these subqueries, providing powerful tools for complex data analysis scenarios.
• Examples and Usage:

SELECT * FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);

• uk.co.certification.simulator.questionpool.PList@225292f0

SELECT * FROM orders o WHERE EXISTS (SELECT 1 FROM customer c WHERE c.id = o.customer_id AND c.region = 'North America');

The LOGIN_HISTORY view in SNOWFLAKE.ACCOUNT_USAGE shows from which IP address a user connected to Snowflake. This view is particularly useful for auditing and monitoring purposes, as it helps administrators track login attempts, successful logins, and the geographical location of users based on their IP addresses.

Reference to Snowflake documentation on LOGIN_HISTORY:

• Monitoring Login Attempts

When deciding to use a secure view, several considerations come into play, especially concerning security and performance:

A. No details of the query execution plan will be available in the query profiler: Secure views are designed to prevent the exposure of the underlying data and the view definition to unauthorized users. Because of this, the detailed execution plans for queries against secure views are not available in the query profiler. This is intended to protect sensitive data from being inferred through the execution plan.

C. Secure views do not take advantage of the same internal optimizations as standard views: Secure views, by their nature, limit some of the optimizations that can be applied compared to standard views. This is because they enforce row-level security and mask data, which can introduce additional processing overhead and limit the optimizer's ability to apply certain efficiencies that are available to standard views.

B. Once created, there is no way to determine if a view is secure or not is incorrect because metadata about whether a view is secure can be retrieved from the INFORMATION_SCHEMA views or by using the SHOW VIEWS command.

D. It is not possible to create secure materialized views is incorrect because the limitation is not on the security of the view but on the fact that Snowflake currently does not support materialized views with the same dynamic data masking and row-level security features as secure views.

E. The view definition of a secure view is still visible to users by way of the information schema is incorrect because secure views specifically hide the view definition from users who do not have the privilege to view it, ensuring that sensitive information in the definition is not exposed.

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

To extract the URL of a directory table on an external stage for further transformation in Snowflake, the GET_ABSOLUTE_PATH function can be used. This function returns the full path of a file or directory within a specified stage, enabling users to dynamically construct URLs for accessing or processing data stored in external stages.

References:

• Snowflake Documentation: Working with Stages

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

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

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.

References:

• Snowflake Documentation: Data Sharing

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.

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 command to list all the file formats for which a user has access privileges in Snowflake is SHOW FILE FORMATS. This command provides a list of all file formats defined in the user's current session or specified database/schema, along with details such as the name, type, and creation time of each file format. It is a valuable tool for users to understand and manage the file formats available for data loading and unloading operations.

References:

• Snowflake Documentation: SHOW FILE FORMATS

Scalar functions in Snowflake (and SQL in general) are designed to return a single value for each invocation. They operate on a single value and return a single result, making them suitable for a wide range of data transformations and calculations within queries.

References:

• Snowflake Documentation: Functions

The Cloud Services layer in Snowflake is responsible for a wide range of services that facilitate the management and use of Snowflake, including:

• D. User authentication: This service handles identity and access management, ensuring that only authorized users can access Snowflake resources.
• E. Infrastructure management: This service manages the allocation and scaling of resources to meet user demands, including the management of virtual warehouses, storage, and the orchestration of query execution.

These services are part of Snowflake's fully managed, cloud-based architecture, which abstracts and automates many of the complexities associated with data warehousing.

References:

• Snowflake Documentation: Overview of Snowflake Cloud Services

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

The Fail-safe retention period for transient and temporary tables in Snowflake is 0 days. Fail-safe is a feature designed to protect data against accidental loss or deletion by retaining historical data for a period after its Time Travel retention period expires. However, transient and temporary tables, which are designed for temporary or short-term storage and operations, do not have a Fail-safe period. Once the data is deleted or the table is dropped, it cannot be recovered.

References:

• Snowflake Documentation: Understanding Fail-safe

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

Top of Form

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.

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

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

By default, the ACCOUNTADMIN role in Snowflake has the ability to create a share from a shared database. This role has the highest level of access within a Snowflake account, including the management of all aspects of the account, such as users, roles, warehouses, and databases, as well as the creation and management of shares for secure data sharing with other Snowflake accounts.

References:

• Snowflake Documentation: Roles

Snowflake's unique architecture is described as a multi-cluster, shared data architecture that leverages massively parallel processing (MPP). This architecture separates compute and storage resources, enabling Snowflake to scale them independently. It does not use a single cluster or rely solely on symmetric multiprocessing (SMP); rather, it uses a combination of shared-nothing architecture for compute clusters (virtual warehouses) and a centralized storage layer for data, optimizing for both performance and scalability.

References:

• Snowflake Documentation: Snowflake Architecture Overview

In Snowflake, semi-structured data is optimally stored using specific data types that are designed to handle the flexibility and complexity of such data. The VARIANT data type can store structured and semi-structured data types, including JSON, Avro, ORC, Parquet, or XML, in a single column. The ARRAY data type, on the other hand, is suitable for storing ordered sequences of elements, which can be particularly useful for semi-structured data types like JSON arrays. These data types provide the necessary flexibility to store and query semi-structured data efficiently in Snowflake.

References:

• Snowflake Documentation: Semi-structured Data Types

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

The layer of Snowflake's architecture associated with virtual warehouses is the Query Processing layer. Virtual warehouses in Snowflake are dedicated compute clusters that execute SQL queries against the stored data. This layer is responsible for the entire query execution process, including parsing, optimization, and the actual computation. It operates independently of the storage layer, enabling Snowflake to scale compute and storage resources separately for efficiency and cost-effectiveness.

References:

• Snowflake Documentation: Snowflake Architecture

The worksheet and database explorer feature in Snowsight allows users to tag frequently accessed worksheets for ease of access. This functionality helps users organize and quickly navigate to the worksheets they use most often, enhancing productivity and streamlining the data exploration and analysis process within Snowsight, Snowflake's web-based query and visualization interface.

References:

• Snowflake Documentation: Snowsight (UI for Snowflake)

The SYSTEM$GET_PRIVATELINK function in Snowflake provides proxy information necessary for configuring PrivateLink connections, which can protect Snowsight as well as other Snowflake services. PrivateLink enhances security by allowing Snowflake to be accessed via a private connection within a cloud provider’s network, reducing exposure to the public internet.

References:

• Snowflake Documentation: PrivateLink Setup

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

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

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

• Purpose: The BUILD_STAGE_FILE_URL function generates a temporary, pre-signed URL that allows you to access a file within a Snowflake stage (internal or external). This URL requires authentication to use.
• Key Points:

• Snowflake Documentation (BUILD_STAGE_FILE_URL): https://docs.snowflake.com/en/sql-reference/functions/build_stage_file_url.html

To access a file URL from an external stage in Snowflake, the minimum permission required is USAGE on the stage object. USAGE permission allows a user to reference the stage in SQL commands, necessary for actions like listing files or loading data from the stage, but does not permit the user to alter or drop the stage.

References:

• Snowflake Documentation: Access Control

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

Snowflake's architecture is unique in that it combines elements of both traditional shared-disk and shared-nothing database architectures. This hybrid approach allows Snowflake to offer the scalability and performance benefits of a shared-nothing architecture (with compute and storage separated) while maintaining the simplicity and flexibility of a shared-disk architecture in managing data across all nodes in the system. This results in an architecture that provides on-demand scalability, both vertically and horizontally, without sacrificing performance or data cohesion.

References:

• Snowflake Documentation: Snowflake Architecture

When initiating a session in Snowflake, the system determines the current role based on the user's connection details and role assignments. If a user specifies a role during the connection, and that role is already granted to them, Snowflake sets it as the current role for the session. Alternatively, if no role is specified during the connection, but the user has a default role assigned, Snowflake will use this default role as the current session role. These mechanisms ensure that users operate within their permissions, enhancing security and governance within Snowflake environments.

References:

• Snowflake Documentation: Understanding Roles

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.

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

The SECURITYADMIN role in Snowflake can manage any object grant globally, including modifying and revoking grants. This role has the necessary privileges to oversee and control access to all securable objects within the Snowflake environment4.

The LOOP type of loop in Snowflake Scripting does not have a built-in termination condition and requires a BREAK statement to stop executing4.

 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

 A Scoped URL permits temporary access to a staged file without the need to grant privileges to the stage or to issue access tokens. It provides a secure way to share access to files stored in Snowflake

Multi-Factor Authentication (MFA) provides increased login security for users connecting to Snowflake. Snowflake’s MFA is powered by Duo Security service, which adds an additional layer of security during the login process.

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. 

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

 In a regular schema within Snowflake, the object owner has the privilege to grant object privileges. The object owner is typically the role that created the object or to whom the ownership of the object has been transferred78.

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

To start configuring Snowflake for Single Sign-On (SSO), the CREATE SECURITY INTEGRATION command is used. This command sets up a security integration object in Snowflake, which is necessary for enabling SSO with external identity providers using SAML 2.01.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

 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.

Unstructured data stored in Snowflake can be retrieved by using SQL functions to generate URLs that point to the data. These URLs can then be used to download the data directly to a client

The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the account level. This parameter determines the minimum number of days Snowflake retains historical data for Time Travel operations

When federated authentication is activated in Snowflake, users authenticate via an external identity provider (IdP) rather than using Snowflake-managed credentials. Therefore, a user with a password defined by Snowflake will be unable to enter a password and must use their IdP credentials to log in.

QUSTION NO: 579

What value provides information about disk usage for operations where intermediate results do not fit in memory in a Query Profile?

A. IO

B. Network

C. Pruning

D. Spilling

Answer: D

In Snowflake, when a query execution requires more memory than what is available, Snowflake handles these situations by spilling the intermediate results to disk. This process is known as "spilling." The Query Profile in Snowflake includes a metric that helps users identify when and how much data spilling occurs during the execution of a query. This information is crucial for optimizing queries as excessive spilling can significantly slow down query performance. The value that provides this information about disk usage due to intermediate results not fitting in memory is appropriately labeled as "Spilling" in the Query Profile.

References:

• Snowflake Documentation on Query Profile and Performance: This section explains the various components of the query profile, including the spilling metric, which indicates disk usage for operations where intermediate results exceed available memory.

 Tags in Snowflake allow users to track sensitive data for compliance, discovery, protection, and resource usage. They enable the categorization and tracking of data, supporting compliance with privacy regulations678. References: [COF-C02] SnowPro Core Certification Exam Study Guide

The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.

References: [COF-C02] SnowPro Core Certification Exam Study Guide

The parameter DATA_RETENTION_TIME_IN_DAYS can be set at the account level to define the minimum number of days Snowflake retains historical data for Time Travel1.

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

 A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide

 Storing JSON data in a table with a VARIANT data type is optimal for querying because it allows Snowflake to leverage its semi-structured data capabilities. This approach enables efficient storage and querying without the need for flattening the data, which can be performance-intensive1.

The LIST and PUT commands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients. LIST is used to display the contents of a stage, and PUT is used to upload files to a stage. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Snowflake’s Time Travel feature is used for the maintenance of historical data, allowing users to access and restore data that has been changed or deleted within a defined period4.

The search optimization service improves the performance of point lookup queries on large tables by using selective filters to quickly return a small number of rows. It creates an optimized data structure that helps in pruning the micro-partitions that do not contain the queried values3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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

Micro-partitioning is a Snowflake function that is maintained separately from the data and supports features such as Time Travel, Secure Data Sharing, and pruning. It allows Snowflake to efficiently manage and query large datasets by organizing them into micro-partitions1.

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.

The Snowflake command to unload the result of a query to a single file is COPY INTO with SINGLE = TRUE followed by a GET command to download the file. This command unloads the query result into a single file in the specified internal stage

 The TO_JSON function is used to convert a VARIANT value into a string containing the JSON representation of the value. This function is suitable for unloading data from a relational table to JSON format. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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.

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

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.

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

 A masking policy in Snowflake consists of a single data type, with one or more conditions, and one or more masking functions. These components define how the data is masked based on the specified conditions3.

 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 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.

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

 SnowCD (Snowflake Connectivity Diagnostic Tool) is the best tool provided by Snowflake for troubleshooting network connectivity issues. It helps diagnose and resolve issues related to connecting to Snowflake services

https://docs.snowflake.com/en/user-guide/snowcd.html#:~:text=SnowCD%20(i.e.%20Snowflake%20Connectivity%20Diagnostic,their%20network%20connection%20to%20Snowflake .

The Snowflake Cloud Services layer coordinates activities within the Snowflake account. It is responsible for tasks such as authentication, infrastructure management, metadata management, query parsing and optimization, and access control. References: Based on general cloud database architecture knowledge.

 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

Resource monitors in Snowflake can be configured at the account and virtual warehouse levels. They are used to track credit usage and control costs associated with running virtual warehouses. When certain thresholds are reached, resource monitors can trigger actions such as sending alerts or suspending warehouses to prevent excessive credit consumption. References: [COF-C02] SnowPro Core Certification Exam Study Guide

In Snowflake, when queries begin to queue in a multi-cluster virtual warehouse, an additional compute cluster starts immediately if the warehouse is set to auto-scale mode. This mode allows Snowflake to automatically add or resume additional clusters as soon as the workload increases, and similarly, shut down or pause the additional clusters when the load decreases

 Snowflake provides Multi-Factor Authentication (MFA) support as an integrated feature, powered by the Duo Security service. This service is managed completely by Snowflake, and users do not need to sign up separately with Duo1

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

 Snowflake supports semi-structured data types, which include OBJECT and VARIANT. These data types are capable of storing JSON-like data structures, allowing for flexibility in data representation. OBJECT can directly contain VARIANT, and thus indirectly contain any other data type, including itself1.

For continuous data loads using Snowpipe, the recommended compressed file size range is between 100-250 MB. This size range is suggested to optimize the number of parallel operations for a load and to avoid size limitations, ensuring efficient and cost-effective data loading

 Periodic rekeying of micro-partitions is a feature that requires the Enterprise Edition of Snowflake or higher. This feature is part of Snowflake’s comprehensive approach to encryption key management, ensuring data security through regular rekeying1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

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.

In Snowflake, a tabular User-Defined Function (UDF) is defined with a return clause that includes the keyword “TABLE.” This indicates that the UDF will return a set of rows, which can be used in the FROM clause of a query. References: Based on my internal knowledge as of 2021.

 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

When file format options are specified in multiple locations, the load operation applies the options in the following order of precedence: first, the COPY INTO TABLE statement; second, the stage definition; and third, the table definition1

In the Admin area of Snowsight, users can analyze query performance, manage Snowflake warehouses, set up and view details about resource monitors, manage users and roles, and administer Snowflake accounts in their organization2.

 In a multi-cluster warehouse with an economy scaling policy, queries will wait in the queue for 2 minutes before another cluster is started. This is to minimize costs by allowing queries to queue up for a short period before adding additional compute resources. References: [COF-C02] SnowPro Core Certification Exam Study Guide

A Snowflake Data Exchange allows groups of accounts to share data privately among the accounts (B), and it supports bidirectional sharing, meaning an account can be both a provider and a consumer of data (E). This facilitates secure and governed data collaboration within a selected group3.

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.

One of the security features of Snowflake includes the periodic rekeying of encrypted data, which is available with the Snowflake Enterprise edition and higher2. This ensures that the encryption keys are rotated regularly to maintain a high level of security. References: [COF-C02] SnowPro Core Certification Exam Study Guide