SnowPro Associate Platform SOL-C01Demo

Snowflake Notebooks support SQL, Python, and Markdown cells. Therefore, a notebook can contain both SQL cells and Python cells. Snowflake worksheets also support Python, so they are not restricted to SQL. Develop and run code in Snowflake Notebooks

Snowflake natively supports JSON and Parquet as semi-structured data formats. NDJSON is handled as newline-delimited JSON, not as a distinct file-format type.

A schema belongs to one database and serves as a logical grouping for database objects such as tables and views. Virtual warehouses are account-level compute resources, not objects contained in schemas.

Snowflake transfers an object's ownership by granting the OWNERSHIP privilege to the receiving role. For a database, the command form is GRANT OWNERSHIP ON DATABASE db_name TO ROLE new_role;.

Snowflake Time Travel provides access to historical table data within its retention period, enabling past versions to be queried and objects to be restored. Fail-safe is for Snowflake-managed disaster recovery and is not directly queryable by users.

The Snowflake LIST command lists files in a specified stage, such as LIST @my_stage;.

Loading with COPY INTO <table> writes staged files into a target table. The files are obtained from a stage, and a running current virtual warehouse supplies the compute resources for the load. Views, data shares, and micro-partitions are not required objects for this operation.

AI_COMPLETE accepts a text prompt and generates a completion with a supported language model. It is Snowflake Cortex’s general-purpose function for generative AI tasks, including creating synthetic data.

The OWNERSHIP privilege gives a role full control over the database, including the authority to manage the database and its access privileges.

A Query ID is the unique identifier for an individual SQL statement. A Session ID can be associated with multiple statements.

Snowflake automatically enables Time Travel with a standard retention period of 1 day (24 hours).

Snowsight is Snowflake’s browser-based web interface for working with data and administering Snowflake.

A Snowflake stage is a location for data files used in loading and unloading. An external stage references a cloud-storage location that Snowflake can access, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage.

Snowflake’s Database Explorer provides a hierarchical browser of databases, schemas, and contained objects, including tables. It shows only objects for which the active role has the required privileges, allowing users to identify accessible tables.

Snowsight’s Monitoring area includes Query History, which lets users review queries executed in the account along with execution details and performance information.

COPY INTO <table> loads data files from a stage into the specified target table. The named CSV file format tells Snowflake how to parse the source file data during that load. Snowflake: Copy data from an internal stage

The QUERY_HISTORY view records query execution information such as execution time and warehouse usage, enabling performance analysis for queries run on a particular virtual warehouse. Snowflake documents that Snowsight can show warehouse load and that query history can be filtered by warehouse to examine execution duration.

The default size for a standard virtual warehouse created in Snowsight or with CREATE WAREHOUSE is X-Small (XSMALL).

Snowsight displays both SQL Text and Query ID by default in the Query History queries table. Session ID, Client Driver, and Incident can be added as optional columns.

USE SCHEMA specifies the active (current) schema for the session, allowing subsequent SQL statements to refer to objects within that schema without fully qualifying their names.

PDF is an unstructured file format. In Snowflake, CSV is structured data, while JSON and Parquet are semi-structured formats; invoice amounts embedded in a PDF do not make the document structured.

Snowsight can infer a file’s metadata schema, including column data types, when creating a table. It also supports loading records from supported structured and semi-structured file types into a table.

Snowflake Notebooks provide SQL and Python cells for writing and executing code. Markdown is also supported for documentation, but JavaScript, Scala, and C++ are not supported Notebook code-cell languages.

Snowflake Notebooks provide a unified, interactive cell-based environment for SQL and Python, enabling both languages to be used within the same development workflow. Snowflake documentation: Create a notebook