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:
Uncorrelated Scalar Subquery:
SELECT * FROM employees WHERE salary > (SELECT AVG(salary) FROM employees); Correlated EXISTS Subquery:
SELECT * FROM orders o WHERE EXISTS (SELECT 1 FROM customer c WHERE c.id = o.customer_id AND c.region = 'North America');

When file format options are specified in multiple locations, the load operation applies the options in the following order of precedence: first, the COPY INTO TABLE statement; second, the stage definition; and third, the table definition1

A well-clustered table in Snowflake means that the data is organized in such a way that related data points are stored close to each other within the micro-partitions. This optimizes query performance by reducing the amount of scanned data. The diagram indicated by option C shows a well-clustered table, as it likely represents a more evenly distributed range of values across the micro-partitions1.
References = Snowflake Micro-partitions & Table Clustering

The search optimization service in Snowflake enhances query performance by maintaining a persistent data structure. This structure indexes the values of table columns across micro-partitions, allowing Snowflake to quickly identify which micro-partitions contain relevant data for a query. By efficiently narrowing down the search space, this service reduces the amount of data scanned during query execution, leading to faster response times and more efficient use of resources.References: Snowflake Documentation on Search Optimization Service

To easily transfer data from a production account to a non-production account in Snowflake within the same cloud provider region, creating a data share is the most efficient approach. Data sharing allows for live, read-only access to selected data objects from the production account to the non-production account without the need to duplicate or move the actual data. This method facilitates seamless access to the data for development, testing, or analytics purposes in the non-production environment.
Reference:
Snowflake Documentation: Data Sharing