According to best practices, how should these requirements be met?

A Snowflake Administrator needs to set up Time Travel for a presentation area that includes facts and dimensions tables, and receives a lot of meaningless and erroneous loT data. Time Travel is being used as a component of the company’s data quality process in which the ingestion pipeline should revert to a known quality data state if any anomalies are detected in the latest load. Data from the past 30 days may have to be retrieved because of latencies in the data acquisition process.

According to best practices, how should these requirements be met? (Select TWO).
A . Related data should not be placed together in the same schema. Facts and dimension tables should each have their own schemas.
B . The fact and dimension tables should have the same DATA_RETENTION_TIME_IN_ DAYS.
C . The DATA_RETENTION_TIME_IN_DAYS should be kept at the account level and never used for lower level containers (databases and schemas).
D . Only TRANSIENT tables should be used to ensure referential integrity between the fact and dimension tables.
E . The fact and dimension tables should be cloned together using the same Time Travel options to reduce potential referential integrity issues with the restored data.

View Answer

Answer: BE

Explanation:

According to the Understanding & Using Time Travel documentation, Time Travel is a feature that allows you to query, clone, and restore historical data in tables, schemas, and databases for up to 90 days.

To meet the requirements of the scenario, the following best practices should be followed:

• The fact and dimension tables should have the same DATA_RETENTION_TIME_IN_DAYS. This parameter specifies the number of days for which the historical data is preserved and can be accessed by Time Travel. To ensure that the fact and dimension tables can be reverted to a consistent state in case of any anomalies in the latest load, they should have the same retention period. Otherwise, some tables may lose their historical data before others, resulting in data inconsistency and quality issues.

• The fact and dimension tables should be cloned together using the same Time Travel options to reduce potential referential integrity issues with the restored data. Cloning is a way of creating a copy of an object (table, schema, or database) at a specific point in time using Time Travel. To ensure that the fact and dimension tables are cloned with the same data set, they should be cloned together using the same AT or BEFORE clause. This will avoid any referential integrity issues that may arise from cloning tables at different points in time.

The other options are incorrect because:

• Related data should not be placed together in the same schema. Facts and dimension tables should each have their own schemas. This is not a best practice for Time Travel, as it does not affect the ability to query, clone, or restore historical data. However, it may be a good practice for data modeling and organization, depending on the use case and design principles.

• The DATA_RETENTION_TIME_IN_DAYS should be kept at the account level and never used for lower level containers (databases and schemas). This is not a best practice for Time Travel, as it limits the flexibility and granularity of setting the retention period for different objects. The retention period can be set at the account, database, schema, or table level, and the most specific setting overrides the more general ones. This allows for customizing the retention period based on the data needs and characteristics of each object.

• Only TRANSIENT tables should be used to ensure referential integrity between the fact and dimension tables. This is not a best practice for Time Travel, as it does not affect the referential integrity between the tables. Transient tables are tables that do not have a Fail-safe period, which means that they cannot be recovered by Snowflake after the retention period ends. However, they still support Time Travel within the retention period, and can be queried, cloned, and restored like permanent tables. The choice of table type depends on the data durability and availability requirements, not on the referential integrity.