Snowflake SOL-C01 - SnowPro Associate: Platform Certification Exam

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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The Database Storage Layer is responsible for persistently storing all customer data in Snowflake. This layer automatically manages micro-partitioning, compression, and metadata. Data is stored in optimized, columnar formats inside cloud object storage (AWS S3, Azure Blob Storage, or GCP Storage). Users cannot manipulate physical storage directly; access is only through SQL operations. Other layers serve different purposes: Cloud Services manages metadata and orchestration, Query Processing Layer executes workloads, and Virtual Warehouses provide compute but do not store data.

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Semi-structured data refers to formats that do not follow a rigid relational schema but still contain structural tags or hierarchies, allowing flexible representation of nested or irregular data. In Snowflake,JSON,Parquet, andXMLare all considered semi-structured because they incorporate metadata, hierarchical fields, or tagged data that can vary across records. JSON offers key–value pairs and nested arrays, making it ideal for logs and API responses. Parquet, being a columnar file format containing both schema definitions and metadata, is optimized for analytics while still supporting semi-structured capabilities such as variable field nesting. XML uses tags and attributes to represent hierarchical content, making it semi-structured as well. On the other hand, CSV represents strictly structured, row/column-based data without inherent metadata or hierarchy, so it is not considered semi-structured. Snowflake treats semi-structured formats by loading them into the VARIANT data type, enabling powerful SQL-based exploration using path notation.

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The Data Exchange feature is not automatically enabled for all Snowflake accounts. Instead, enabling this capability—particularly for private exchanges—typically requires submitting a request toSnowflake Support. Snowflake evaluates account type, region, and compliance requirements before enabling the Data Exchange functionality. Snowflake administrators cannot independently enable this through Snowsight, nor is there a SQL command available to turn it on. Once activated, account administrators can create private exchanges, manage listings, and define which Snowflake accounts can participate. This activation step ensures secure, intentional use of data-sharing capabilities at an account level.

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Directory tables allow Snowflake to expose metadata about files stored in an external stage, enabling users to query file listings using SQL. To enable a directory table, the correct approach is to update an existing external stage using:

ALTER STAGE my_stage SET DIRECTORY = (ENABLE = TRUE);

This enables automatic synchronization of file metadata from cloud providers such as AWS S3, Azure Blob, or Google Cloud Storage.

Incorrect options:

Directory tables arenot enabled by default.

There is noCREATE DIRECTORY TABLEcommand in Snowflake.

Creating a view cannot enable directory table functionality.

Once enabled, users can query:

SELECT * FROM DIRECTORY(@my_stage);

to retrieve file names, sizes, MD5 hashes, and timestamps.

COPY INTO @stage writes query results or table data into files stored in either internal or external stages.

Unloading cannot target a local filesystem directly, cannot load into another table (use INSERT instead), and does not write to streams.

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A view in Snowflake represents avirtual tablewhose contents are defined by a stored SQL query. Views allow users to encapsulate transformation logic, simplify complex joins, enforce column-level security, and provide curated datasets for downstream consumers.

Views donotstore data themselves; the underlying tables store the data, and Snowflake executes the view’s query each time the view is referenced. This makes views ideal for abstraction layers, semantic modeling, and separating compute costs across user groups.

Incorrect options:

Snowflake views do not store raw data.

Views have no role in authentication or network configuration.

Views help streamline business logic and control data access efficiently.

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Snowflake’s architecture separates storage and compute, enabling:

• Compute scaling (up/down or multi-cluster) without changing storage

• Storage expansion without affecting compute

• Cost optimization by paying for compute only when needed

This separation does not impact governance, latency, or data loading requirements.

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CREATE STAGE is the correct Snowflake DDL command for creating a named internal stage. Stages hold files for loading/unloading. The command defaults to internal storage unless external parameters are provided.

Other options are not valid Snowflake syntax.

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A multi-cluster warehouse raises Snowflake’s capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness. Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs. Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.

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