SOL-C01 SnowPro Associate: Platform Certification Exam Questions and Answers

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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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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In Snowflake Notebooks, Python and SQL operate in an integrated environment. Python variable substitution is performed usingstring placeholders or f-string interpolationwithin SQL statements. This allows dynamic query construction where Python values are embedded directly into SQL code executed through the Snowpark session.

Example:

table_name = "CUSTOMERS"

session.sql(f"SELECT * FROM {table_name}").show()

This method enables parameterized, context-aware SQL generation—ideal for iterative development, automation, and pipeline construction. It avoids manual text editing and provides consistency across Python and SQL execution layers.

Incorrect responses:

Network settings and HTML are unrelated to variable handling.

Editing data files does not apply to dynamic query parameterization.

Variable substitution is essential for combining logic, iteration, and conditional flows within notebooks.

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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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The Query Processing Layer executes SQL statements using virtual warehouses and massively parallel processing. It handles query optimization, execution, and scaling. The Cloud Services Layer parses and plans queries but does not execute them. Storage persists data, and the Client Layer is external to Snowflake architecture.

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COPY INTO <title> bulk-loads data from internal or external stages into Snowflake tables. It supports transformations, validation, and multiple file formats.

It does not replicate databases, manage roles, or create warehouses.

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Semi-structured data like JSON stored in a VARIANT column can be queried directly usingpath notation, either dot notation or bracket notation.

Examples:

Dot notation:SELECT data:customer.name FROM table;

Bracket notation:SELECT data['customer']['name'] FROM table;

Snowflake automatically interprets the JSON structure, making relational extraction unnecessary. Complex fields can be accessed through functions such as FLATTEN, OBJECT_KEYS, ARRAY_SIZE, and TYPEOF.

Incorrect options:

Converting JSON to a relational table is optional, not required.

Stored procedures are not necessary for JSON querying.

WHERE on the full VARIANT cannot precisely extract fields.

Thus, path notation is the native, efficient method.

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Snowflake is best described as anAI Data Cloud Platform. While Snowflake originated as a cloud data warehouse, its capabilities have expanded to include secure data sharing, application development, machine learning workloads, unstructured data processing, Snowpark, and integrated LLM/AI functions (such as Snowflake Cortex and Streamlit). This broad ecosystem supports analytic workloads, governance, multi-cloud architecture, and AI-driven applications. It is not simply an ETL tool or a transactional database.

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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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Snowflake usesrole-based access control (RBAC), where privileges are assigned to roles rather than individual users. All of the listed privileges (SELECT, INSERT, MONITOR) can be granted to a role.

SELECTallows reading data from tables, views, or materialized views.

INSERTallows adding new rows into a table.

MONITORallows viewing query history, warehouse status, and resource usage.

In addition, roles can receive privileges such as CREATE TABLE, USAGE, MODIFY, OPERATE, OWNERSHIP, and many others. Users inherit all privileges assigned to the roles they possess, enabling scalable security design.

Thus, all listed privileges can be granted to Snowflake roles.

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Every query executed in Snowflake is assigned a uniqueQuery ID, a system-generated identifier that allows users to retrieve, analyze, and reference the query. Query IDs support debugging, profile retrieval, audit tracking, workload analysis, and support cases.

Transaction IDs are only assigned to transactional operations and do not uniquely identify all queries.

Session IDs identify user sessions—not individual queries. Many queries can occur in the same session.

Account ID is the identifier for the entire Snowflake account and does not relate to specific queries.

Thus,Query IDis the unique attribute used to identify each query in Query History.

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Schemas in Snowflake serve asorganizational containersfor database objects such as tables, views, sequences, stages, file formats, and functions. They existwithin a database, forming Snowflake’s logical hierarchy: account → database → schema → object.

Schemas are not limited to a single role; access depends on granted privileges. Schemas are not immutable—administrators can rename, alter, or drop them. Schemas do not contain virtual warehouses; warehouses exist independently at the account level.

Thus, the correct characteristics are that schemas organize objects and are contained within a database.

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Snowflake uses theVARIANTdata type as the native mechanism for storing semi-structured data formats such as JSON, XML, Avro, ORC, and Parquet. VARIANT supports hierarchical, nested structures and allows querying via dot or bracket path notation without requiring schema-on-write.

JSON is not a Snowflake data type—JSON documents arestored insideVARIANT.

VARIANT allows:

Mixed structured and semi-structured content

Efficient storage through Snowflake’s columnar compression

Schema-on-read flexibility

Querying nested structures without flattening

VARCHARstores plain text only.

BLOBis reserved for binary data and not suitable for interactive querying.

This design eliminates the need for preprocessing or schema enforcement at ingestion time.

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The USE SCHEMA command sets the active schema context for the current session. After it is executed, any unqualified object names (for example, SELECT * FROM my_table) are resolved within that schema. This reduces the need to fully qualify object names with database and schema each time and ensures that statements reference the expected logical container.

CREATE SCHEMA is used to create a new schema. ALTER SCHEMA and GRANT OWNERSHIP are used to modify schema properties or transfer ownership, respectively. USE SCHEMA does not alter structure or ownership; it simply changes the context in which subsequent SQL statements are interpreted.

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Snowflake’s Cortex LLM includestask-specific functions, meaning each performs a well-defined AI operation with predictable outputs. Examples include:

TRANSLATE– Converts text between languages; deterministic and domain-independent.

CLASSIFY_TEXT / AI_CLASSIFY– Assigns text to predefined categories, ideal for sentiment, topics, or routing tasks.

PARSE_DOCUMENT– Extracts structured information from documents (PDFs, invoices, receipts, contracts) including layout-aware content.

These functions are optimized for reliability, reproducibility, and governance, making them suitable for production pipelines.

COUNT_TOKENSis not task-specific—it’s a utility function used to estimate LLM token usage rather than perform a primary AI task.

Thus, TRANSLATE, CLASSIFY_TEXT, and PARSE_DOCUMENT are the correct task-specific functions.

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Snowflake’s object hierarchy maintains a structured namespace. At the top is thedatabase, which serves as a container for related schemas. Eachschemaorganizes logical groupings of tables, views, stages, file formats, and other database objects.

Tablesstore persistent, structured data.

Viewsprovide virtual representations of data through stored SQL queries. They do not store data themselves but reference underlying tables.

The hierarchical structure is:

..

To create a new schema in Snowsight, users must follow the hierarchical navigation of Snowflake’s object structure. The correct sequence is:

Data → Database → Create Schema

Steps:

Open theDatatab in the left navigation.

Select aDatabasewhere the schema will be created.

ChooseCreate → Schema.

Enter the schema name and (optionally) description.

ClickCreate.

Incorrect options:

“Add Data” is used for loading files, not creating schema objects.

“Data → Create Schema” is incomplete because Snowflake must knowwhich databasethe schema belongs to.

“Data Product” is unrelated to object creation.

Thus, Snowflake requires explicit database context before schema creation.

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A single VARIANT value in Snowflake has a maximum size limit of16 MB(uncompressed). This limit applies to semi-structured data stored in VARIANT, such as JSON, XML, Avro, and Parquet objects.

This limit ensures efficient storage, query performance, and micro-partitioning. Exceeding the size results in load or processing errors. For ingestion, when loading large JSON files, it’s recommended to break them into multiple objects or use JSON Lines formatting.

Snowflake has introduced an optional behavior change bundle (2025_03), increasing the limit to128 MBfor accounts that enable it, but the standard default remains 16 MB.

Thus, under default behavior,16MBis the correct maximum value size.

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