SOL-C01 Exam Dumps - SnowPro Associate: Platform Certification Exam

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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The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for Snowflake objects such as tables, schemas, and databases. It specifies the number of days that historical data (prior versions of rows or dropped objects) is retained and accessible via Time Travel. Within this retention window, users can query data “as of” a previous time, restore dropped objects, or clone objects at a historical point.

DATE_OUTPUT_FORMAT determines the display format for date values and is unrelated to historical retention. TIMEZONE affects how timestamps are interpreted and displayed, not how long data history is preserved. USE_CACHED_RESULT governs whether Snowflake may return cached query results, not Time Travel behavior.

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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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Scalingup or downrefers tovertical scaling, meaning the warehouse’s compute size is increased or decreased. For example, moving fromSmall → Medium → Largeincreases CPU, memory, and I/O capacity, enabling faster processing for compute-intensive workloads.

Vertical scaling improves single-query performance, large ETL jobs, complex joins, or transformations. It does not improve concurrency unless multi-cluster mode is also used.

Horizontal scaling (scaling out/in), by contrast, adjusts thenumber of clustersand is used for concurrency.

Region selection is fixed at account creation and cannot be changed by resizing a warehouse. Storage movement is unrelated to compute rescaling.

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The Snowflake multi-cluster shared data architecture separates compute and storage, enablingindependent scaling of each. Compute resources (virtual warehouses) operate independently from the centralized storage layer, allowing users to increase compute power without impacting storage costs—or vice versa. This flexibility provides major advantages for cost optimization and workload performance tuning.

The architecture also supportsnear-unlimited concurrency and elasticity. When many users or workloads run simultaneously, Snowflake can automatically add additional clusters to a multi-cluster warehouse, ensuring that no queries experience queuing or performance degradation. This capability is crucial for BI dashboards, ETL pipelines, and large organizations with varied usage patterns.

Columnar storage (option D) is indeed a Snowflake feature but is not unique to the multi-cluster architecture. Enhanced security arrives via Snowflake’s platform-wide mechanisms, not specifically because of multi-cluster architecture. Unstructured data loading (option B) is also not governed by multi-cluster behavior.

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Snowsight provides adrag-and-dropinterface for uploading data from a local machine into an internal stage. This is done via: Data → Add Data → Load Files into a Stage. Users can upload multiple files and choose the destination stage. This convenient UI eliminates the need for command-line tools such as SnowSQL. Snowsight supports both local file uploads and cloud storage references, not just cloud sources.

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The correct SQL command for creating a new database in Snowflake isCREATE DATABASE database_name;. This initializes a new logical container for schemas and objects. USE DATABASE switches the active database context but does not create one. Snowflake does not support the shorthand CREATE DB or USE DB syntax. Therefore, CREATE DATABASE is the correct and only valid command for creating a database.

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In Snowflake Marketplace and Data Exchange, aprovideris any Snowflake user or organization that publishes and shares datasets, data products, or applications with other Snowflake consumers. Providers control access to their listings, configure data sharing models, and can offer either free or paid data products. They manage listing descriptions, update schedules, and refresh logic, ensuring customers or subscribers receive up-to-date and reliable information. Providers can be enterprises, analytics vendors, public data contributors, or research institutions. They are distinct from consumers, who access or use the shared data. Providers also do not handle Snowflake platform infrastructure—that is Snowflake’s responsibility. Snowflake employees are not categorized as providers unless they publish official Snowflake datasets. Thus, the primary purpose of a provider is to share curated data offerings on the Marketplace or within private exchanges.

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