Free Practice Questions for Snowflake SOL-C01 Exam

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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Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. Thesearch bar at the top of the Marketplaceis a primary entry point, enabling keyword-based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.

Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.

Browsing categories and tagssupports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.

Contacting Snowflake Supportis not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.

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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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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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