Free Practice Questions for Snowflake SOL-C01 Exam

Aschemain Snowflake is a logical container inside a database that groups related objects. It can containtables,views,stages,file formats,sequences,streams,tasks, and other database objects. Schemas allow structured organization, privilege management, and separation of workloads.

Tablesstore structured data.

Viewsare virtual tables defined by queries on other objects.

Stagesstore data files for loading/unloading.

Virtual warehouses are compute resources, not database objects, and therefore exist outside schemas. Warehouses belong to the account-level namespace and are managed independently of databases and schemas.

Schemas play a key role in RBAC and object naming through fully qualified names: database.schema.object.

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Snowflake’s foundational security capability isalways-on end-to-end encryption, applied to all customer data both at rest and in transit. Snowflake encrypts data stored in micro-partitions using AES-256, and all network communication uses TLS encryption. Key management is automated through Snowflake’s hierarchical key model, providing additional rotation, rekeying, and defense-in-depth mechanisms.

While RBAC, masking policies, and network policies are crucial components of Snowflake security, encryption is the platform’s guaranteed, baseline, mandatory protection feature. It applies universally to all data, regardless of workload, role configuration, or user settings.

Option A: automated masking is optional and policy-driven, not a primary universal mechanism.

Option B: network perimeter defense exists but is not the platform’s core foundation.

Option C: RBAC controls access but does not protect data itself.

Thus, Snowflake’s primary and most universal security capability is end-to-end encryption.

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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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TheLISTcommand displays all files available in a stage—internal, external, user, or table stages.

It is typically used prior to COPY INTO to confirm which files are available for loading.

SHOW TABLES lists tables; SHOW GRANTS shows privileges; GRANTS queries show user-role assignments—not LIST.

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VARIANT supports nested, flexible structures and enables querying via JSON path notation. It is optimized for semi-structured formats (JSON, Avro, ORC, Parquet).

STRING, VARCHAR, and TEXT store plain text and cannot represent nested structures without conversion.

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Snowflake supports two official stage types:

• Internal Stages – User stages, table stages, and named internal stages stored inside Snowflake.

• External Stages – Refer to cloud storage (S3, Azure Blob, GCS) using integration credentials.

Managed, permanent, and temporary stages are not official Snowflake classifications.

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TheCOPY INTOcommand supports loading structured and semi-structured data from various Snowflake stages. Supported file formats includeCSV,JSON,Parquet,Avro,ORC, and others. Each format integrates with Snowflake’s file format definitions, enabling configuration for delimiters, compression, headers, and parsing rules.

CSVis widely used for tabular datasets and supports flexible parsing via FILE_FORMAT options such as FIELD_DELIMITER, SKIP_HEADER, and NULL_IF.

JSONis loaded natively into VARIANT columns, allowing nested structures to be queried immediately through path notation.

Parquet, a high-efficiency columnar format, is optimized for analytic workloads and provides excellent compression and schema evolution benefits.

XLS is not supported, as Snowflake does not support Excel spreadsheets directly. XLS/XLSX files must be converted to CSV or Parquet before loading.

The COPY INTO architecture ensures performant ingestion across multi-node compute clusters.

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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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Afully qualified namein Snowflake specifies the exact location of an object and follows this structure:

..

This enables precise referencing, especially in environments with multiple databases or schemas.

Components:

Database name– the top-level container.

Schema name– the logical grouping of related objects.

Table or view name– the specific object being referenced.

Example:

SALES_DB.PUBLIC.ORDERS

Roles arenotpart of object names. Roles control authorization, while fully qualified names handle namespace resolution. Using fully qualified names helps avoid ambiguity in pipelines, stored procedures, views, and cross-database queries.

Snowflake also supports partially qualified names depending on active context (current database and schema).

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The COPY INTO

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