PARSE_DOCUMENT supports two processing modes:OCRandLAYOUT. OCR mode performs Optical Character Recognition, extracting raw text from scanned documents, images within PDFs, or low-quality text-based documents. It is ideal for scenarios like contract ingestion, receipt processing, or older scanned documents. LAYOUT mode extracts structured layout elements---tables, paragraphs, lines, bounding boxes---preserving the original document's spatial organization. This enables downstream analytical tasks such as table reconstruction or semantic segmentation of document content. OMR (Optical Mark Recognition) is not a supported feature, and ''CONTENT'' is not a valid mode. By supporting OCR and LAYOUT modes, Snowflake Cortex provides robust document intelligence capabilities directly within the Snowflake environment.

PARSE_DOCUMENT extracts text and, depending on mode, layout information from supported document formats. The function currently supportsPDF,PPTX, andDOCXfiles. For PDFs, it can perform OCR to extract scanned text or use LAYOUT mode to detect tables and structured regions. For PPTX files, it extracts slide text and content. For DOCX files, the function captures text and applies layout analysis. XLSX files are not supported---Excel content requires different extraction methods. PARSE_DOCUMENT enables automated document intelligence workflows entirely inside Snowflake, eliminating the need for external OCR or parsing services.

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In Snowflake's logical hierarchy, tables reside within schemas, and schemas reside within databases. This structured arrangement allows clear organization, access management, and separation of workloads or business domains. A schema functions as a container that holds tables, views, functions, sequences, stages, and file formats. When users reference a table, they typically qualify it using the full three-part name: database.schema.table.

Stages are used to store or reference files, not to hold structured database tables. Virtual warehouses provide compute power for executing queries and performing DML operations but do not store data. File formats define how staged files should be interpreted (CSV, JSON, Parquet, etc.) and are unrelated to where tables physically reside.

Snowflake separates compute and storage, so while compute happens in virtual warehouses, all persistent data---including tables---is managed in Snowflake's centralized storage layer, organized through schemas and databases. This organization ensures consistency, ease of management, and predictable access control.

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The only valid Snowflake SQL command among the options forcreating a schemais:

CREATE SCHEMA schema_name;

This command creates a schema within the currently active database. Optional modifiers like IF NOT EXISTS or OR REPLACE can be used to control behavior.

Other options:

USE SCHEMAswitches to an existing schema; it does not create one.

USE SCHis invalid because SCH is not a Snowflake keyword.

CREATE SCHis invalid; SCH is not a valid abbreviation.

Thus, the only valid schema creation command isCREATE SCHEMA.

Zero-Copy Cloning is ametadata-only operation, creating instantaneous clones without copying underlying data. Clones are fully writable and operate independently. Snowflake only allocates storage when data diverges due to modifications. It does not require significant storage nor duplicate physical data.