Snowflake Updated SOL-C01 Exam Questions and Answers by ayzal

The Query Processing layer of Snowflake is wherevirtual warehouses operate, so warehouse sizing parameters (X-Small to 6X-Large) fall under this layer. Warehouse size determines compute power, concurrency, and performance behavior for SQL workloads. Administrators configure warehouse size based on workload intensity, response time requirements, and cost considerations.

Serverless compute limits and micro-partition limits belong to storage and services layers. Table types (permanent, transient, temporary) are storage-level configurations, not part of Query Processing.

Thus, warehouse sizing is the correct parameter configured at the Query Processing layer.

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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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SHOW GRANTS lists privileges assigned to roles, users, objects, or shares. It is essential for permissions auditing and RBAC management.

SHOW USERS lists users, not privileges.

Warehouse usage is in ACCOUNT_USAGE views.

Load history is retrieved via COPY_HISTORY or LOAD_HISTORY.

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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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