SOL-C01 Practice Questions

A multi-cluster warehouse raises Snowflake’s capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness. Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs. Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.

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Snowflake supports zero-copy cloning at thedatabase,schema, andtablelevels. Cloning creates a new metadata reference to existing data without physically duplicating micro-partitions, significantly reducing storage cost and clone creation time. Cloning adatabasereplicates all schemas and tables within it at that moment in time. Cloning aschemareplicates all objects inside that schema, including tables and views. Cloning atablecreates an isolated version of that table for development, testing, or analysis. Importantly,rolescannot be cloned because they are account-level security objects, not data objects. Privileges must be manually granted or recreated for cloned objects. Cloning enables rapid, efficient creation of consistent environments for workload isolation and testing.

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Snowsight supports two worksheet types:SQL worksheetsandPython worksheets. SQL worksheets allow users to execute queries, create objects, and perform data analysis using ANSI SQL and Snowflake-specific extensions. Python worksheets, powered by Snowpark, allow users to write Python code that interacts directly with Snowflake tables, data frames, and machine learning workflows.

Java, Scala, and JavaScript are supported via Snowpark APIs or UDF development, but they cannot be used as worksheet languages. Worksheets are designed for interactive analysis, visualization, and iterative development, with native runtimes only for SQL and Python.

Thus, only SQL and Python worksheets can be created within Snowsight.

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Snowflake supports bothTOPandLIMITas valid SQL mechanisms for restricting the number of rows returned by a query. They function similarly but are used in different syntactic positions.

LIMITappears at the end of the query:

SELECT * FROM my_table LIMIT 10;

This tells Snowflake to return only the specified number of rows.

TOPis used in the SELECT clause:

SELECT TOP 10 * FROM my_table;

Both approaches are equivalent in functionality and are supported to maintain compatibility with ANSI SQL and other SQL dialects.

The other options are not correct row-limiting mechanisms:

•SELECTis a required keyword but does not limit rows on its own.

•GETis not a SQL keyword for row restriction; GET is used for downloading files from internal stages.

•ROW_NUMBERis a window function used for ranking rows, not constraining row count.

Therefore, TOP and LIMIT are the only correct SQL keywords in Snowflake for controlling how many rows are returned by a query.

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