Snowflake COF-C02 SnowPro Core Certification Exam Practice Test

Answer : C

In a scenario where a Snowflake user queries only a small number of rows that require significant processing and the data in the table does not change frequently, the most effective way to optimize performance is by creating a materialized view based on the query. Materialized views store the result of the query and can significantly reduce the computation time for queries that are executed frequently over unchanged data.

Why Materialized Views: Materialized views precompute and store the result of the query. This is especially beneficial for queries that require heavy processing. Since the data does not change frequently, the materialized view will not need to be refreshed often, making it an ideal solution for this use case.

Implementation Steps:

To create a materialized view, use the following SQL command:

CREATE MATERIALIZED VIEW my_materialized_view AS SELECT ... FROM my_table WHERE ...;

When the query is run, Snowflake uses the precomputed results from the materialized view, thus skipping the need for recalculating the data and improving query performance.

Answer : C

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

Answer : D

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.

Answer : A

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.

Answer : D

In Snowflake, a user within a reader account primarily has read-only access to the shared data. Reader accounts are created to enable third parties or separate business units to access and query data shared with them without allowing them to modify the underlying data. This means a user with a reader account can perform operations like querying shared data to analyze or report on it but cannot load new data, update existing data, or create new shares. This setup is crucial for maintaining data governance and security while enabling data sharing and collaboration. References: Snowflake Documentation on Reader Accounts

Answer : A

Column-level security is a feature that allows fine-grained control over access to specific columns within a table. This is particularly useful for managing sensitive data and ensuring that only authorized users can view or manipulate certain pieces of information. According to my last update, this feature was available in the Enterprise Edition or higher editions of Snowflake.

References: Based on my internal data as of 2021, column-level security is an advanced feature typically reserved for higher-tiered editions like the Enterprise Edition in data warehousing solutions such as Snowflake.

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

Answer : A

In Snowflake, if a temporary table and a permanent table have the same name within the same schema, the temporary table takes precedence over the permanent table within the session where the temporary table was created4.