Snowflake Updated COF-C02 Exam Questions and Answers by jad

For minimizing the cost of large tables that are used to store transitory data, which does not need to be protected against failures because it can be reconstructed outside of Snowflake, the best table type to use is Transient. Transient tables in Snowflake are designed for temporary or transitory data storage and offer reduced storage costs compared to permanent tables. However, unlike temporary tables, they persist across sessions until explicitly dropped.

Why Transient Tables: Transient tables provide a cost-effective solution for storing data that is temporary but needs to be available longer than a single session. They have lower data storage costs because Snowflake does not maintain historical data (Time Travel) for as long as it does for permanent tables.

Creating a Transient Table:

To create a transient table, use the TRANSIENT keyword in the CREATE TABLE statement:

CREATE TRANSIENT TABLE my_transient_table (...);

Use Case Considerations: Transient tables are ideal for scenarios where the data is not critical, can be easily recreated, and where cost optimization is a priority. They are suitable for development, testing, or staging environments where data longevity is not a concern.

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

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 search optimization service in Snowflake significantly improves the performance of range searches and equality searches. Range searches involve looking for values within a specific range (e.g., BETWEEN, <, >). Equality searches involve looking for values that match a specific value (e.g., =).

References:

Snowflake Documentation: Search Optimization Service