Snowflake SOL-C01 - SnowPro Associate: Platform Certification Exam

To create a new stage in Snowsight, users navigate through:Data → Database → Schema → Create Stage. Within the schema, selecting the "+" icon opens the option to create a stage. Users may configure internal or external stages, encryption settings, and directory table options. Other paths listed reference creating schemas rather than creating stages and are therefore incorrect.

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Snowflake provides two main types of stages:Internal stagesandExternal stages. Internal stages reside inside Snowflake and include user stages, table stages, and named internal stages. These offer convenient, secure storage within Snowflake’s managed environment. External stages reference cloud storage locations such as AWS S3, Azure Blob Storage, or Google Cloud Storage, enabling integration with external data repositories. Snowflake does not define a stage type called “Public” or “Private,” although stages can be granted secure access. Thus, the correct types are Internal and External stages.

Snowflake uses theVARIANTdata type as the native mechanism for storing semi-structured data formats such as JSON, XML, Avro, ORC, and Parquet. VARIANT supports hierarchical, nested structures and allows querying via dot or bracket path notation without requiring schema-on-write.

JSON is not a Snowflake data type—JSON documents arestored insideVARIANT.

VARIANT allows:

Mixed structured and semi-structured content

Efficient storage through Snowflake’s columnar compression

Schema-on-read flexibility

Querying nested structures without flattening

VARCHARstores plain text only.

BLOBis reserved for binary data and not suitable for interactive querying.

This design eliminates the need for preprocessing or schema enforcement at ingestion time.

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Snowflake Notebooks provide an interactive environment where developers and analysts can combineSQL,Python, visualization libraries, and documentation. Use cases include exploratory analysis, ETL prototyping, data engineering workflows, machine learning model development, and dashboard creation using Streamlit components.

Notebooks allow mixing of SQL and Python cells with shared session state, enabling smooth transitions between Snowpark DataFrames, SQL queries, visual charts, and markdown explanations. They enhance reproducibility and collaboration, supporting versioning, parameterization, and seamless Snowflake compute integration.

Incorrect options:

Raw data is stored in Snowflake tables, not in notebooks.

User authentication is handled by the Cloud Services Layer.

Managing cloud storage is automatic and not a notebook responsibility.

Thus, Snowflake notebooks are an end-to-end development and analytics interface.

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Snowflake includes severalsystem-defined RBAC rolesto provide structured access control across the platform.

ACCOUNTADMINis the highest-privileged role, governing the entire account, including billing, replication, failover groups, and object ownership inheritance.

SECURITYADMINis responsible for user, role, and privilege management, including granting and revoking roles, password policies, and MFA enforcement. It is essential for identity lifecycle governance.

USERADMINmanages user creation, role creation, and user authentication administration. It is commonly assigned to identity administrators.

CUSTOM_ROLEis not a Snowflake system role; it is a user-created role, allowing organizations to design fine-grained, domain-specific access policies.

These predefined roles form the foundation of Snowflake’s security model and support least-privilege implementations.

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To create a new schema in Snowsight, users must follow the hierarchical navigation of Snowflake’s object structure. The correct sequence is:

Data → Database → Create Schema

Steps:

Open theDatatab in the left navigation.

Select aDatabasewhere the schema will be created.

ChooseCreate → Schema.

Enter the schema name and (optionally) description.

ClickCreate.

Incorrect options:

“Add Data” is used for loading files, not creating schema objects.

“Data → Create Schema” is incomplete because Snowflake must knowwhich databasethe schema belongs to.

“Data Product” is unrelated to object creation.

Thus, Snowflake requires explicit database context before schema creation.

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TheData Storage Layerin Snowflake stores all data in acompressed, encrypted, columnar formatdesigned for analytical efficiency. Snowflake automatically reorganizes ingested data into micro-partitions optimized for pruning and high-performance scans. The compression and structuring are fully automated, requiring no tuning or indexing by the user.

Another foundational feature iselastic storage, made possible by Snowflake’s cloud-native architecture. Storage automatically scales up or down based on data volume without user intervention, and is separated from compute. This ensures cost efficiency and immediate availability of capacity for large datasets.

Incorrect options:

In-memory storageis not a Snowflake feature; long-term data is persisted in cloud object storage.

Elastic Cloudis not a Snowflake architectural term.

The Data Storage Layer is designed for durability, scalability, and optimal query performance.

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Snowflake'sINFORMATION_SCHEMAcontains metadata views about databases, schemas, tables, columns, constraints, and other objects. To retrieve a list of tables, the correct view isINFORMATION_SCHEMA.TABLES, which returns:

Table name

Schema name

Table type (BASE TABLE, VIEW, MATERIALIZED VIEW)

Creation timestamp

Owner and retention properties

The query:

SELECT * FROM INFORMATION_SCHEMA.TABLES;

returns metadata for all tables in thecurrent databasecontext.

INFORMATION_SCHEMA.COLUMNS returns column-level metadata, not table listings.

PUBLIC.INFORMATION_SCHEMA is invalid—INFORMATION_SCHEMA is not inside PUBLIC schema.

PUBLIC.TABLES is not a system object unless created manually.

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Snowflake supports unloading semi-structured data specifically intoJSONandParquetformats. JSON preserves semi-structured hierarchy and nested fields, making it ideal for interchange, backup, and integration with downstream systems. Parquet provides columnar, compressed, and metadata-rich storage well-suited for analytical reprocessing in data lakes or other systems. CSV is not semi-structured and cannot represent nested data accurately. XML, although a semi-structured format, is not supported by Snowflake as an unload format. When performing COPY INTO , users can specify FILE_FORMAT with TYPE=JSON or TYPE=PARQUET to export semi-structured content efficiently.

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AUTO_SUSPEND minimizes cost by suspending compute after a specified idle period.

It prevents unnecessary credit consumption while ensuring compute resumes automatically when needed.

Performance improvements depend on warehouse size, not suspension settings.

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