Snowflake SOL-C01 Exam Dumps

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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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's database storage layer is responsible for encrypting data at rest. All persisted data---whether structured, semi-structured, or unstructured---is encrypted using strong encryption algorithms such as AES-256. This process is automatic and transparent to users, ensuring that files and micro-partitions stored in Snowflake-managed cloud storage are encrypted by default. Data in transit is protected separately through TLS.

Virtual warehouses provide compute resources to execute queries and do not perform storage-level encryption. Data loading utilities (such as COPY INTO and client tools) orchestrate data movement but do not handle at-rest encryption. The Cloud Services layer manages metadata, session management, security policies, and query optimization, but the actual encryption of stored data is part of the storage subsystem.

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The LIMIT clause limits the number of rows Snowflake returns from a query. It is commonly used to preview datasets, improve performance when exploring large tables, and paginate results. LIMIT may be combined with OFFSET to skip a number of rows. Sorting is performed with ORDER BY, filtering with WHERE, and grouping with GROUP BY---not with LIMIT. Therefore, restricting returned rows is the primary purpose of LIMIT.

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ThePARSE_JSON()function converts a valid JSON string into aVARIANTvalue. This allows Snowflake to store and query nested, hierarchical data using dot and bracket notation.

Example:

SELECT PARSE_JSON('{'name':'John','age':30}') AS data;

After parsing, fields can be accessed like:

data:name or data['age']

Incorrect options:

TO_JSON()converts VARIANT to a JSON string (opposite direction).

CONVERT_JSONandEXTRACT_JSONare not Snowflake functions.

PARSE_JSON is essential for dynamically loading, transforming, or analyzing JSON content from files, streams, or external applications.