Snowflake SOL-C01 - SnowPro Associate: Platform Certification Exam

Executing anINSERTcommand in a Snowflake worksheet requires three components to be explicitly selected:

Active Warehouse– Provides compute resources to process the query. Without an active warehouse, DML operations cannot execute.

Database– The table receiving the INSERT must reside in the active database context.

Schema– The targeted table must be within a selected schema.

All three define the fully qualified context for locating and writing to the correct object. Snowflake’s compute/storage separation ensures compute is only used when warehouses are active. Schema and database selection ensure correct namespace resolution.

====================================================

Snowflake supports several native data types for managing temporal information:

DATE – Stores dates only (year, month, day) without any time component.

TIMESTAMP – Represents date and time values, including several variants:

• TIMESTAMP_NTZ (no time zone)

• TIMESTAMP_LTZ (local time zone)

• TIMESTAMP_TZ (explicit time zone)

DATETIME – An alias for TIMESTAMP_NTZ, storing both date and time.

These data types enable powerful time-based analytics and comparisons.

❌TIMEDATE is not a valid data type.

❌Python and JavaScript are programming languages, not Snowflake data types.

====================================================

In Snowflake’s Secure Data Sharing model, the accountsharingdata is known as thedata provider. Providers create shares that include selected objects (tables, views, secure views, etc.) and grant access to consumer accounts.

Adata consumerreceives the shared data.Data readeranddata administratorare not defined terms in Snowflake’s sharing architecture.

==================

Scalingin/outin Snowflake refers to modifying thenumber of compute clustersassociated with a multi-cluster virtual warehouse. Scalingoutincreases cluster count to accommodate higher concurrency or workload spikes, allowing more queries to run simultaneously without queuing. Scalinginreduces cluster count during periods of lower demand, optimizing compute usage and costs. This is distinct fromscaling up/down, which refers to changing warehouse size (e.g., Small, Medium). Scaling does not involve data movement or region changes; warehouse compute is stateless and operates independently of storage. Multi-cluster warehouses allow Snowflake to automatically add or remove clusters based on demand when auto-scale policies are configured.

=======================================

Snowflake’s RBAC security model revolves aroundUsers,Roles, andPrivileges. Users represent individual identities or service accounts. Roles group privileges and are assigned to users. Privileges define specific actions that can be performed on objects (e.g., SELECT, INSERT, OWNERSHIP). “Permissions” is not a formal RBAC component—permissions are effectively the result of privileges assigned to roles. Therefore, the three core RBAC components are Users, Roles, and Privileges.

=======================================

The default Time Travel retention period for most objects in Snowflake is1 day(24 hours). Time Travel enables access to historical versions of data after updates, deletes, or drops. It also allows cloning databases, schemas, and tables at previous points in time, and recovering dropped objects.

While Snowflake Enterprise Edition and higher tiers allow retention periods up to 90 days, this extended window is not the default—administrators must explicitly configure it for each table, schema, or database.

Retention periods of 7, 45, or 90 days are possible only with higher service editions; the default for all accounts and objects remains 1 day unless explicitly overridden.

==================

Snowflake’s file format options allow granular control over CSV ingestion. The SKIP_HEADER = 1 parameter instructs Snowflake to ignore the first line of the file, typically containing column headers. This is the correct and official parameter within FILE FORMAT objects or inline COPY INTO options. Other options listed—such as SKIP_FILE_HEADER, IGNORE_HEADER, or HEADER_SKIP—are not valid Snowflake parameters. When SKIP_HEADER is applied, Snowflake begins reading data rows starting from line 2. This ensures proper alignment between file contents and table structure during ingestion. SKIP_HEADER can also be set to values greater than 1 if multiple header rows exist. This behavior is essential when loading structured data where the header row is not intended for table consumption.

=======================================

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.

==================

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.

==================