Snowflake Notebooks support multiple cell types for development and documentation.Pythoncells allow users to perform data transformations, analysis, and Snowpark operations.SQLcells enable executing Snowflake SQL queries directly against tables and views.Markdowncells support formatted documentation within notebooks—useful for narrative explanations and commentary. The option “Markup” is not a supported cell type in Snowflake Notebooks. These three supported types allow users to blend analytics, code execution, and documentation in a unified environment.

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Snowsight’s file-loading interface:

•Automatically detects data typesby inspecting column values.

•Loads (inserts) file contents directly into a table, either a new table or an existing one.

Snowsight does not automatically create permanent file formats, does not merge rows, and does not optimize warehouse-level performance.

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A Snowflake database is alogical containergrouping one or more schemas. It does not store data physically in the traditional sense—Snowflake abstracts physical storage into its cloud-native micro-partitioned storage layer. Because schemas contain tables, views, and other objects, a database organizes schemas logically, enabling governance, naming conventions, and security boundaries.

A virtual warehouse is compute, not storage. A “collection of tables and views” better describes a schema, not a database. Snowflake separates logical structure from physical storage, so a database is not a physical file repository.

Thus, a database is best defined as a logical grouping of schemas.

The CREATE FILE FORMAT command creates a file format object in Snowflake that defines how data files should be interpreted when loading or unloading. Parameters in the definition include file type (for example, CSV, JSON, PARQUET), field delimiters, compression, quoting rules, and other parsing options. Once created, this file format object can be referenced by COPY INTO commands or external table definitions to ensure consistent handling of files.

Deleting a file format is performed using DROP FILE FORMAT. Modifying an existing file format is done with ALTER FILE FORMAT. Creating a new table is achieved with CREATE TABLE. Therefore, the purpose of CREATE FILE FORMAT is specifically to define how Snowflake should process data files.

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Data listings in the Snowflake Marketplace can be offered asfree or paiddepending on how the provider chooses to distribute the data. Providers can use usage-based pricing, subscription models, or make datasets available at no cost. Listings are not restricted to static data; Snowflake supportslive data sharing, enabling instant access to continuously updated datasets without copying or ETL. Listings are accessible to any Snowflake user with the appropriate marketplace access—not exclusively administrators. This flexibility makes the Marketplace a powerful ecosystem where organizations can seamlessly obtain external datasets for analytics, machine learning, and reporting needs. Providers define terms of use, pricing, and refresh schedules, while Snowflake manages the secure data-sharing infrastructure.

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Snowflake’sTime Travelenables querying historical data as it existed at a prior point in time. This is accomplished using theATorBEFOREclause within the FROM statement:

SELECT * FROM my_table AT (TIMESTAMP = > ' 2025-06-18 10:00:00 ' );

SELECT * FROM my_table BEFORE (OFFSET = > -3600);

Time Travel supports access via timestamp, statement ID, or time offset. This is essential for auditing, recovery, and reconstructing past states for analysis.

Incorrect options:

RESTOREis used to recover dropped objects, not to query history.

BACKUPis not a Snowflake SQL clause.

HISTORYis not a valid SQL clause.

Thus, AT | BEFORE enables precise historical queries.

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Snowsight supports two worksheet types:SQL worksheetsandPython worksheets. SQL worksheets allow users to execute queries, create objects, and perform data analysis using ANSI SQL and Snowflake-specific extensions. Python worksheets, powered by Snowpark, allow users to write Python code that interacts directly with Snowflake tables, data frames, and machine learning workflows.

Java, Scala, and JavaScript are supported via Snowpark APIs or UDF development, but they cannot be used as worksheet languages. Worksheets are designed for interactive analysis, visualization, and iterative development, with native runtimes only for SQL and Python.

Thus, only SQL and Python worksheets can be created within Snowsight.

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Snowflake supports theEXCLUDEkeyword to simplify queries when excluding certain columns from a SELECT * operation. SELECT * EXCLUDE (column1, column2) reduces verbosity and enhances maintainability, especially when table schemas evolve. Explicitly listing all columns is possible but inefficient. Snowflake does not support REMOVE functions for columns nor an OMIT clause. EXCLUDE is the correct and official mechanism.

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In Snowflake Python worksheets, settings include theHandlerandReturn Type. The Handler identifies the specific Python function that serves as the entry point for execution—essential when deploying code as stored procedures or user-defined functions. The Return Type defines how Snowflake interprets the output, such as STRING, VARIANT, or TABLE, ensuring proper result rendering. “Return Value” is not a worksheet setting but rather the outcome produced by the code itself, and “Output” is not a configuration option.

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Snowflake Cortex AI provides built-in AI functions and tools designed to work natively with unstructured and structured data. Two key capabilities are:

• Extract and classify text using functions like PARSE_DOCUMENT, EXTRACT_TEXT, and classification models. Cortex can process documents, identify relevant fields, and convert unstructured content into usable structured formats.

• Simplify unstructured data workflows by combining document extraction, vector search, summarization, and AI reasoning tools (e.g., Cortex Analyst, Cortex Search) directly inside Snowflake without external services.

It does not provide Marketplace data sharing features, which belong to Snowflake’s Data Sharing platform. Loading semi-structured data is a core Snowflake capability using VARIANT and COPY INTO—not Cortex-specific. Enhancing data security is a platform-wide feature, not a Cortex function.

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