Snowflake SnowPro-Core - SnowPro Core Certification Exam

Snowflake's Continuous Data Protection (CDP) features encompass several mechanisms designed to protect data and ensure its availability and recoverability. Among these features, the one that has associated costs is Fail-safe.

Fail-safe is an additional layer of protection that kicks in after the Time Travel period expires. While Time Travel allows users to access historical data within a defined retention period (which can vary from 1 to 90 days depending on the Snowflake edition), Fail-safe provides a further 7 days (for a total of 7 additional days beyond the Time Travel period) during which Snowflake retains the data. This period is primarily intended for Snowflake's internal operations to recover data in the event of extreme scenarios, such as significant operational failures, and is not directly accessible by customers for data recovery purposes.

The associated costs with Fail-safe arise because Snowflake continues to store the data beyond the customer-specified Time Travel period, thereby incurring additional storage costs. It's important to note that while users do not incur direct costs for enabling Fail-safe (as it is an automatic feature of Snowflake), the extended storage of data during this period contributes to overall storage costs.

References:

Snowflake Documentation on Continuous Data Protection: Continuous Data Protection (CDP)

Snowflake Documentation on Fail-safe: Understanding Fail-safe

For users assigned the ACCOUNTADMIN role, Snowflake recommends enforcing Multi-Factor Authentication (MFA) to enhance security. The ACCOUNTADMIN role has extensive permissions, making it crucial to secure accounts held by such users against unauthorized access. MFA adds an additional layer of security by requiring a second form of verification beyond just the username and password, significantly reducing the risk of account compromise.References: Snowflake Security Best Practices

The GET_PRESIGNED_URL file function in Snowflake provides a URL with access to a file on a stage without requiring authentication and authorization. This is particularly useful for sharing data files stored in Snowflake stages with external parties securely and conveniently. The presigned URL generated by this function gives temporary access to the file, which expires after a specified duration.

Example usage of GET_PRESIGNED_URL:

SELECT GET_PRESIGNED_URL('', '');

This function generates a URL that can be used to directly access a file in the stage, making it easier to share data without compromising security.

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.

 Users are responsible for data storage costs in Snowflake until the data expires from the Fail-safe period. Fail-safe is the final stage in the data lifecycle, following Time Travel, and provides additional protection against accidental data loss. Once data exits the Fail-safe state, users are no longer billed for its storage

 In Snowflake, a task allows a user to execute a single SQL statement/command using a predefined schedule (B). Tasks are used to automate the execution of SQL statements at scheduled intervals.

 In Snowflake, a schema is a logical grouping of database objects, which can include streams and external tables. A stream is an object that allows users to query data that has changed in specified tables or views, and an external table is a table that references data stored outside of Snowflake. Roles, warehouses, users, and shares are not contained within a schema. References: SHOW OBJECTS, Database, Schema, & Share DDL

 In Snowflake’s access control model, the default owner of an object is the role that was used to create the object. This role has the OWNERSHIP privilege on the object and can grant access to other roles1

 The GET command is used to download files from a Snowflake stage to a local folder on a client’s machine2.

Multi-cluster virtual warehouses in Snowflake are designed to manage user and query concurrency needs. They allow for the allocation of additional clusters of compute resources, either statically or dynamically, to handle increased loads and reduce or eliminate the queuing of concurrent queries2.

https://docs.snowflake.com/en/user-guide/warehouses-multicluster.html#:~:text=Multi%2Dcluster%20warehouses%20enable%20you,during%20peak%20and%20off%20hours .

The ResultSet cache is leveraged to quickly return results for repeated queries. Actions that prevent leveraging this cache include stopping the virtual warehouse that the query is running against (B) and executing the RESULTS_SCAN() table function (D). Stopping the warehouse clears the local disk cache, including the ResultSet cache1. The RESULTS_SCAN() function is used to retrieve the result of a previously executed query, which bypasses the need for the ResultSet cache.

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 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

 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.

 Temporary and transient tables in Snowflake do not have a Fail-safe period. Once the session ends or the tables are dropped, the data is purged and not recoverable1.