The default Time Travel retention period for most objects in Snowflake is1 day(24 hours). Time Travel enables access to historical versions of data after updates, deletes, or drops. It also allows cloning databases, schemas, and tables at previous points in time, and recovering dropped objects.

While Snowflake Enterprise Edition and higher tiers allow retention periods up to 90 days, this extended window is not the default—administrators must explicitly configure it for each table, schema, or database.

Retention periods of 7, 45, or 90 days are possible only with higher service editions; the default for all accounts and objects remains 1 day unless explicitly overridden.

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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.

The Snowsight Monitoring tab provides a centralized view of virtual warehouse usage metrics, enabling administrators and developers to evaluate how compute resources are being consumed. This includes critical insights such as credit usage, query load, concurrency levels, average queue times, execution durations, and auto-scaling activity (for multi-cluster warehouses). These metrics help determine whether a warehouse is correctly sized, whether concurrency issues are occurring, or whether workloads require scaling up or adding clusters.

Query history is available in a different section—“Activity → Query History”—not under Monitoring. Time Travel snapshots are not visualized within Monitoring; Time Travel is controlled via retention parameters and accessed with SQL (AT/BEFORE clauses). Schema change history is also not part of Monitoring and instead is discoverable through ACCOUNT_USAGE or specific metadata views.

The Monitoring tab exists specifically to help evaluate warehouse performance and resource consumption, enabling optimization of compute spending and better workload management.

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Snowflake Notebooks provide native integration with Streamlit, allowing users to build interactive data apps directly inside Snowflake. Users can call Streamlit functions such as st.write(), st.dataframe(), st.button(), and st.slider() within Python cells. No external HTML, scripting, or network configuration is required. Streamlit components execute seamlessly in the notebook environment, enabling dashboards and interactive visualizations on top of Snowflake data.

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The correct SQL syntax to create a zero-copy clone of an existing table is:

CREATE TABLE MY_TABLE_CLONE CLONE MY_TABLE;

This command instantly creates a new table that references the same underlying micro-partitions as the original. Because of Snowflake’smetadata-only cloning, no storage is consumed at the time of creation. Storage only increases when either the original or the clone diverges through DML operations, following acopy-on-writemodel.

Cloning is available for multiple object types—tables, schemas, databases, stages, streams, tasks, and more. This capability enables rapid creation of development sandboxes, QA environments, rollback copies, or controlled experimentation without duplicating data.

Incorrect options:

“COPY TABLE” is not a valid Snowflake command.

BACKUP/RESTORE are not Snowflake SQL commands.

RESTORE applies only to Time Travel or Fail-safe, not to cloning.

Thus, the CLONE keyword is the only correct method for zero-copy duplication.

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Snowflake’s Cloning feature provides the ability to create azero-copyclone of tables, schemas, or entire databases. This means that the cloned object references the same underlying micro-partition data as the original, so no duplicate storage costs are incurred upon creation. Instead, Snowflake uses metadata pointers to reference the existing data. If changes occur in the original or cloned objects, Snowflake performs copy-on-write operations to maintain isolation while still optimizing storage. This cloning mechanism is extremely valuable for development, testing environments, analytics validation, and troubleshooting historical states. It is not a real-time backup tool; backup and restore are handled through Time Travel and Fail-safe. Cloning does not grant or manage permissions nor copy the entire Snowflake account; it works solely at the database, schema, and table object levels.

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Snowsight provides an intuitive UI-based workflow for loading data, enabling drag-and-drop file upload, schema inference, previewing records, automatic table creation, and loading into existing tables. Users can load CSV, JSON, Parquet, AVRO, ORC, and other supported formats without writing COPY INTO commands manually.

Snowsight also generates the equivalent SQL code, helping users understand and reuse ingestion scripts in pipelines. It eliminates the need for external tools such as SnowSQL CLI or custom ETL scripts.

Incorrect statements:

External tools are not required.

Manual storage configuration is unnecessary; Snowflake abstracts all storage.

Snowsight supports a wide variety of formats, not limited ones.

Thus, the key advantage is asimple, guided data-loading workflow, especially useful for new Snowflake users.

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