Snowflake SOL-C01 Exam Questions

The PARSE_JSON() function ingests a string containing JSON text and converts it into Snowflake'sVARIANTdata type, enabling the JSON to be queried, navigated, and transformed using SQL. Snowflake does not store JSON in its raw textual representation; instead, VARIANT allows Snowflake to apply optimized parsing, indexing, and querying operations against semi-structured content. This function is particularly useful when JSON arrives inline (e.g., supplied directly within SQL statements or loaded from CSV files containing JSON strings). PARSE_JSON does not perform data loading from stages---that is handled through COPY INTO---nor does it convert JSON into XML. Once JSON is converted to VARIANT, Snowflake allows access to nested structures using dot notation, bracket notation, and functions like FLATTEN(). Thus, the function serves as a bridge between raw JSON strings and Snowflake's relational and analytical capabilities.

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LIST @stage_name displays all files present in a stage (internal or external).

PUT uploads local files to an internal stage.

GET downloads files from an internal stage.

COPY INTO loads or unloads data but does not list files.

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For Snowflake Standard Edition accounts, the maximum Time Travel retention period is1 day(24 hours). Time Travel allows users to query historical data, restore dropped objects, and clone objects at a point in time. Although the retention period can be adjusted at the account, database, schema, or table level, Standard Edition restricts the upper limit to 1 day. A value of 0 disables Time Travel entirely. A 7-day retention period applies to Fail-safe, not Time Travel, and Fail-safe is a disaster-recovery mechanism managed exclusively by Snowflake. The 90-day Time Travel retention is available only for Enterprise Edition and above. Therefore, the correct maximum retention period for Standard Edition is 1 day.

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When auto-suspend is enabled on a virtual warehouse, Snowflake automatically suspends (stops) the warehouse after it has been idle for the configured period of time. Once suspended, the warehouse stops consuming compute credits, thereby reducing costs, since Snowflake bills only for active compute usage. When new queries arrive, auto-resume (if enabled) restarts the warehouse automatically.

Auto-suspend does not change the warehouse size; scaling up or down is a separate configuration. The warehouse is not dropped---its metadata and settings remain intact. Costs may be reduced and made more efficient but are not guaranteed to ''stabilize month-to-month,'' as the bill still depends on actual usage and workload patterns.

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Semi-structured data refers to formats that do not follow a rigid relational schema but still contain structural tags or hierarchies, allowing flexible representation of nested or irregular data. In Snowflake,JSON,Parquet, andXMLare all considered semi-structured because they incorporate metadata, hierarchical fields, or tagged data that can vary across records. JSON offers key--value pairs and nested arrays, making it ideal for logs and API responses. Parquet, being a columnar file format containing both schema definitions and metadata, is optimized for analytics while still supporting semi-structured capabilities such as variable field nesting. XML uses tags and attributes to represent hierarchical content, making it semi-structured as well. On the other hand, CSV represents strictly structured, row/column-based data without inherent metadata or hierarchy, so it is not considered semi-structured. Snowflake treats semi-structured formats by loading them into the VARIANT data type, enabling powerful SQL-based exploration using path notation.

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