Free Snowflake SOL-C01 Practice Exam with Questions & Answers | Set: 2

Changing a Snowflake virtual warehouse from Medium to Large increases the compute resources allocated to that warehouse—such as CPU cores, memory, and I/O bandwidth. This process is calledscaling up, or vertical scaling. It is typically performed to improve performance for complex queries, large joins, or computationally heavy workloads.

Scaling down is the reverse—reducing warehouse size from Large to Medium or similar.

Scaling out refers to adding more clusters to a multi-cluster warehouse, enabling increased concurrency rather than increasing power per cluster.

Scaling in is the reverse of scaling out—reducing the number of active clusters.

Thus, increasing the size from Medium to Large is vertical scaling, also known as scaling up.

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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’szero-copy cloningcreates a metadata-only copy of an object—database, schema, table, or many other object types. At creation time, the cloneshares the same underlying micro-partitionsas the source, consuming virtually no additional storage.

Storage consumption occurs only wheneither the clone or the original object is modified, a behavior known ascopy-on-write. This makes cloning ideal for development, testing, QA, troubleshooting, backup creation, or controlled sandboxing.

Incorrect statements:

Clones do not duplicate storage at creation.

Cloning applies to tables, schemas, databases, and other supported objects.

Clones do not synchronize with the original; after creation they diverge independently.

This architecture enables quick, cost-efficient replication of environments and data.

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ADatabaseis the highest logical container for schemas and their objects in Snowflake.

Hierarchy:

Account → Database → Schema → Tables, Views, Stages, etc.

Schemas organize objects within a database; tables and views do not contain objects.

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Snowflake’s architecture is built on three primary layers that separate and optimize storage, compute, and services. TheData Storagelayer stores all structured and semi-structured data in Snowflake’s optimized, compressed columnar format. Snowflake manages file organization, metadata, and optimizations automatically, ensuring efficient storage management. TheQuery Processinglayer is implemented through virtual warehouses that independently execute queries using MPP (massively parallel processing) compute clusters. These warehouses scale elastically based on workload requirements. TheCloud Serviceslayer manages metadata, authentication, access control, query optimization, and infrastructure orchestration. This separation allows independent scaling of compute and storage, optimizing performance while simplifying administration.

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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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Snowflake uses the COPY INTO

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