Ace Your COF-C02 SnowPro Core Certification Exam

Snowflake recommends considering adding a clustering key to a table when its size reaches 1 Terabyte (TB) or larger. Clustering keys help optimize the storage and query performance by organizing the data in a table based on the specified columns. This is particularly beneficial for large tables where data retrieval can become inefficient without proper clustering.

• Why Clustering Keys Are Important: Clustering keys ensure that data stored in Snowflake is physically ordered in a way that aligns with the most frequent access patterns, thereby reducing the amount of scanned data during queries and improving performance.
• Recommendation Basis: The recommendation for tables of size 1 TB or larger is based on the observation that smaller tables generally do not benefit as much from clustering, given Snowflake's architecture. However, as tables grow in size, the benefits of clustering become more pronounced.
• Implementing Clustering Keys:

CREATE TABLE my_table (... ) CLUSTER BY (column1, column2);

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ALTER TABLE my_table CLUSTER BY (column1, column2);

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);

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

The primary factor that determines the cost of using a virtual warehouse in Snowflake is D. The length of time the compute resources in each cluster run. Snowflake's pricing model for compute usage is based on the concept of Snowflake credits, which are consumed based on the time virtual warehouses are running and the size of the warehouses. The more compute resources are utilized and the longer they run, the more credits are consumed, which directly impacts the cost.

References:

• Snowflake Documentation on Virtual Warehouses: Understanding and Managing Warehouse Credit Usage

The correct function to find the query ID of the second query executed in the current session is SELECT LAST_QUERY_ID(-2). The LAST_QUERY_ID function returns the query ID for the most recent query executed in the session when called with no arguments. When used with an argument, it can retrieve the ID of previous queries within the same session, where -2 would reference the second most recent query executed.

References:

• There's a clarification needed here; Snowflake's documentation indicates LAST_QUERY_ID() function does not accept arguments. It returns the ID of the last query executed in the session. To find the query ID of the second last executed query, users typically need to track query IDs manually or use session history views.