Legit SOL-C01 Exam Download

An external stage is the Snowflake object that encapsulates a secure connection to external cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. It stores the location (URL or bucket path) and, where required, credentials or role-based access configuration, and may also reference a file format. External stages are used as the source or target for COPY INTO operations when loading from or unloading to external storage.

An external table provides a logical SQL interface to data stored externally but relies on a stage for connectivity; it does not itself define the connection. A directory table exposes metadata about files stored in a stage, not the connection. A named file format defines parsing rules (type, delimiter, compression) but has no knowledge of or connection to a specific external storage location.

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Snowflake supports bothTOPandLIMITas valid SQL mechanisms for restricting the number of rows returned by a query. They function similarly but are used in different syntactic positions.

LIMITappears at the end of the query:

SELECT * FROM my_table LIMIT 10;

This tells Snowflake to return only the specified number of rows.

TOPis used in the SELECT clause:

SELECT TOP 10 * FROM my_table;

Both approaches are equivalent in functionality and are supported to maintain compatibility with ANSI SQL and other SQL dialects.

The other options are not correct row-limiting mechanisms:

•SELECTis a required keyword but does not limit rows on its own.

•GETis not a SQL keyword for row restriction; GET is used for downloading files from internal stages.

•ROW_NUMBERis a window function used for ranking rows, not constraining row count.

Therefore, TOP and LIMIT are the only correct SQL keywords in Snowflake for controlling how many rows are returned by a query.

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Snowflake’s Python Worksheets require theSnowpark for Pythonpackage. Snowpark integrates deeply with Snowflake’s compute engine, enabling DataFrame operations, UDF creation, ML workflows, and direct execution of Python logic inside Snowflake compute.

While additional libraries like Pandas, NumPy, and Matplotlib may be available in worksheets, they arenot requiredto enable the Python execution environment. Snowpark is the core computational interface that connects Python to Snowflake’s data and compute layers.

It enables:

Distributed data processing

Transformations expressed in Python but executed inside Snowflake

Access to Snowflake tables as DataFrames

Integration with Snowpark ML and Cortex functions

Thus, Snowpark is the essential package for Python worksheet support.

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Snowsight provides an intuitive UI-based workflow for loading data, enabling drag-and-drop file upload, schema inference, previewing records, automatic table creation, and loading into existing tables. Users can load CSV, JSON, Parquet, AVRO, ORC, and other supported formats without writing COPY INTO commands manually.

Snowsight also generates the equivalent SQL code, helping users understand and reuse ingestion scripts in pipelines. It eliminates the need for external tools such as SnowSQL CLI or custom ETL scripts.

Incorrect statements:

External tools are not required.

Manual storage configuration is unnecessary; Snowflake abstracts all storage.

Snowsight supports a wide variety of formats, not limited ones.

Thus, the key advantage is asimple, guided data-loading workflow, especially useful for new Snowflake users.

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