SOL-C01 Exam Dumps - Snowflake SnowPro Advanced Certification Questions and Answers

The correct SQL command for removing a schema in Snowflake is:

DROP SCHEMA temp_schema;

This command deletes the schema and all objects contained within it, including tables, views, stages, file formats, and sequences. Snowflake performs this operation atomically, ensuring metadata consistency during the drop process. Users can also includeIF EXISTSor theCASCADEkeyword to handle dependencies more explicitly:

DROP SCHEMA IF EXISTS temp_schema CASCADE;

This safely handles scenarios where the schema may not exist or contains objects that would normally block deletion.

Incorrect options:

DROP DATABASE temp_schemaremoves an entire database, not a schema.

DELETE SCHEMAis not valid SQL; SQL uses DROP for schema removal.

DROP VIEW temp_schemaapplies only to removing a view object.

Dropping a schema requires USAGE and OWNERSHIP privileges, typically granted to roles such as SYSADMIN or ACCOUNTADMIN.

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TheAccountis the top-level Snowflake container encompassing:

• All databases

• All schemas

• All compute resources (warehouses)

• All roles, users, and governance structures

All other objects exist within the Account context.

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Auto-scale enables Snowflake to dynamically start or stop clusters in a multi-cluster warehouse depending on workload pressure. When concurrency rises, Snowflake adds clusters; when demand decreases, it scales back down. This provides seamless performance during peak workloads and efficient resource usage during off-peak times. Auto-scale does not require manual action—it is fully automated once configured. It is not limited to standard warehouses; multi-cluster mode is available in Enterprise Edition and above. Ultimately, auto-scale ensures elasticity and cost-efficient compute allocation.

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When auto-suspend is enabled on a virtual warehouse, Snowflake automatically suspends (stops) the warehouse after it has been idle for the configured period of time. Once suspended, the warehouse stops consuming compute credits, thereby reducing costs, since Snowflake bills only for active compute usage. When new queries arrive, auto-resume (if enabled) restarts the warehouse automatically.

Auto-suspend does not change the warehouse size; scaling up or down is a separate configuration. The warehouse is not dropped—its metadata and settings remain intact. Costs may be reduced and made more efficient but are not guaranteed to “stabilize month-to-month,” as the bill still depends on actual usage and workload patterns.

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Snowflake Notebooks provide an integrated, interactive development environment that enables users to write SQL and Python code, visualize results, and document analysis—all in the same workspace. They support data visualizations, charts, tables, and Streamlit-based interactions. Notebooks run entirely within Snowsight and require no manual hardware configuration. They are not CLI-based and support much richer displays than plain text. This integration streamlines the entire data exploration, transformation, and modeling workflow.

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Snowsight includes a modern, fully capableSQL editordesigned for rich query development, execution, and analysis. The editor offers features such as syntax highlighting, intelligent auto-complete, contextual error display, and result-set visualization. Users can organize work with worksheets, folders, tagging, and versioning-like behavior.

Snowsight is built for both simple and complex SQL workloads. It supports multi-statement execution, script-like workflows, stored procedure development, materialized views, and advanced analytics queries.

Unsupported options include:

Manual data storage configuration: Snowflake abstracts storage management completely.

Pre-built queries only: Snowsight allows fully custom SQL.

Limited to simple queries: It supports enterprise workloads, BI query chains, and operational SQL.

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A Snowflake database is a top-level container that can include schemas, and within those schemas it can contain objects such astables,views,stages,functions,procedures, anddatabase roles. However,global account-level roles(such as SYSADMIN, SECURITYADMIN, PUBLIC, or custom account roles) do not reside inside a database. These roles are created and managed at the Snowflake account level, not within any specific database. In contrast,database rolesare scoped within a specific database and can exist inside it. Views and tables are standard database objects. Therefore, the only option that a Snowflake database cannot contain is an account-levelRole.

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Changing a Snowflake virtual warehouse from Medium to Large increases the compute resources allocated to that warehouse—such as CPU cores, memory, and I/O bandwidth. This process is calledscaling up, or vertical scaling. It is typically performed to improve performance for complex queries, large joins, or computationally heavy workloads.

Scaling down is the reverse—reducing warehouse size from Large to Medium or similar.

Scaling out refers to adding more clusters to a multi-cluster warehouse, enabling increased concurrency rather than increasing power per cluster.

Scaling in is the reverse of scaling out—reducing the number of active clusters.

Thus, increasing the size from Medium to Large is vertical scaling, also known as scaling up.

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TheACCOUNTADMINrole is Snowflake’s highest-privileged system-defined role. It provides complete administrative authority across the entire Snowflake account. Its functions include:

Managing global account parameters, replication settings, business continuity, failover groups, and region configurations

Administering billing, resource monitoring, and governance

Granting and revoking privileges across all objects and roles

Overseeing role hierarchy, including SECURITYADMIN and SYSADMIN

It is typically reserved for platform owners and security/governance teams, following least-privilege principles.

“Create and manage databases” is primarily a SYSADMIN responsibility.

“Monitor query performance” can be accomplished by roles with MONITOR privileges; it is not exclusive to ACCOUNTADMIN.

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Scalingin/outin Snowflake refers to modifying thenumber of compute clustersassociated with a multi-cluster virtual warehouse. Scalingoutincreases cluster count to accommodate higher concurrency or workload spikes, allowing more queries to run simultaneously without queuing. Scalinginreduces cluster count during periods of lower demand, optimizing compute usage and costs. This is distinct fromscaling up/down, which refers to changing warehouse size (e.g., Small, Medium). Scaling does not involve data movement or region changes; warehouse compute is stateless and operates independently of storage. Multi-cluster warehouses allow Snowflake to automatically add or remove clusters based on demand when auto-scale policies are configured.

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