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Amulti-cluster warehouseprovides Snowflake’s highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.

Improved concurrencyis the primary benefit—large numbers of simultaneous queries are distributed across clusters.

Automatic scaling(scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.

Incorrect options:

Storage costs are unaffected because compute and storage are independent.

Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.

The design enables smooth operation in high-demand enterprise environments.

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Business Critical Edition includes advanced compliance certifications such as HIPAA, PCI DSS, HITRUST, and additional encryption and security controls.

Standard and Enterprise editions lack these compliance guarantees.

VPS provides even stronger isolation but builds on Business Critical features.

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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 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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Snowflake supportsNon-Materialized Views(standard views),Materialized Views, andSecure Views. Non-materialized views compute results dynamically at query time. Materialized views store precomputed results, improving performance for repeated analytical queries. Secure views protect underlying query logic and metadata from being exposed, ensuring privacy when sharing datasets. The option “UnMaterialized” is not a Snowflake concept and does not exist as a view type.

Snowflake’s Python Worksheets require theSnowpark for Pythonpackage. Snowpark integrates deeply with Snowflake’s compute engine, enabling DataFrame operations, UDF creation, ML workflows, and direct execution of Python logic inside Snowflake compute.

While additional libraries like Pandas, NumPy, and Matplotlib may be available in worksheets, they arenot requiredto enable the Python execution environment. Snowpark is the core computational interface that connects Python to Snowflake’s data and compute layers.

It enables:

Distributed data processing

Transformations expressed in Python but executed inside Snowflake

Access to Snowflake tables as DataFrames

Integration with Snowpark ML and Cortex functions

Thus, Snowpark is the essential package for Python worksheet support.

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Roles gain access to objects in Snowflake throughdirect privilege grantsor throughinheritancewithin a role hierarchy. A role may be explicitly granted privileges such as SELECT, OWNERSHIP, or USAGE. Alternatively, if a role is assigned to another role, it inherits all privileges of its parent role. Neither network settings nor data storage processes determine role access.

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The PARSE_DOCUMENT function is part of Snowflake Cortex AI and is designed specifically to extract text, layout information, and structured elements from unstructured documents, especially PDFs. It supports OCR-based extraction for scanned files and layout-aware extraction to preserve tables, headings, and format structure.

Its purpose is not PII detection; Snowflake does not provide built-in automatic PII identification via PARSE_DOCUMENT. It does not identify candidate data for directory tables and is unrelated to JSON parsing—Snowflake uses PARSE_JSON for JSON data.

PARSE_DOCUMENT is primarily used for workflows such as contract analysis, invoice extraction, document classification, compliance automation, and downstream AI enrichment.

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ADatabaseis the highest logical container for schemas and their objects in Snowflake.

Hierarchy:

Account → Database → Schema → Tables, Views, Stages, etc.

Schemas organize objects within a database; tables and views do not contain objects.

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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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