The correct answer is A. Set the warehouse scaling policy to ECONOMY .
A multi-cluster warehouse can use Auto-scale mode to add or remove clusters based on query concurrency.
To help control credit consumption, Snowflake provides an Economy scaling policy.
Why A is correct:
The ECONOMY scaling policy is more conservative than the STANDARD scaling policy. It starts additional clusters only when Snowflake estimates there is enough query load to keep the new cluster busy for a meaningful amount of time. This helps reduce unnecessary cluster starts and can lower credit usage.
Why the other options are incorrect:
B). Setting MIN_CLUSTER_COUNT = 1 can help avoid running multiple clusters constantly, but it does not specifically control Auto-scale behavior as effectively as the Economy scaling policy.
C). Setting AUTO_SUSPEND = 0 disables automatic suspension, which can increase credit consumption.
D). Setting both minimum and maximum cluster count to 2 forces two clusters and removes Auto-scale flexibility. This can increase cost and does not control consumption efficiently.
Official Snowflake documentation reference:
Snowflake documentation explains that the Economy scaling policy conserves credits by starting additional clusters only when enough query load exists to justify them.
Reference: Snowflake Documentation - Multi-cluster warehouses; Snowflake Documentation - Scaling policy; SnowPro Core Study Guide - Snowflake Account and Resource Management.

Valid sub-clauses to the OVER clause for a window function in SQL are:
* C. ORDER BY: This clause specifies the order in which the rows in a partition are processed by the window function. It is essential for functions that depend on the row order, such as ranking functions.
* D. PARTITION BY: This clause divides the result set into partitions to which the window function is applied. Each partition is processed independently of other partitions, making it crucial for functions that compute values across sets of rows that share common characteristics.
These clauses are fundamental to defining the scope and order of data over which the window function operates, enabling complex analytical computations within SQL queries.
References:
* Snowflake Documentation: Window Functions

Snowflake does not require source data files to be no larger than 16MB. In fact, Snowflake recommends that for optimal load performance, data files should be roughly 100-250 MB in size when compressed. However, it is not recommended to load very large files (e.g., 100 GB or larger) due to potential delays and wasted credits if errors occur. Smaller files should be aggregated to minimize processing overhead, and larger files should be split to distribute the load among compute resources in an active warehouse.
References: Preparing your data files | Snowflake Documentation

In Snowflake's data sharing model, any full Snowflake account can both provide and consume shared data. Additionally, the data consumer pays only for the compute resources used to query the shared data. No actual data is copied or transferred between accounts, and shared data does not take up any storage in a consumer account, so the consumer does not pay for data storage1.
Reference = Introduction to Secure Data Sharing | Snowflake Documentation