Snowflake SOL-C01 - SnowPro Associate: Platform Certification Exam

TheSKIP_HEADERoption instructs Snowflake to ignore a defined number of initial rows, typically the column header row in CSV files. For example:

CREATE FILE FORMAT my_fmt

TYPE='CSV'

SKIP_HEADER=1;

When used with COPY INTO, Snowflake loads only the actual data rows while excluding header metadata. This prevents header values from being inserted as data and ensures consistent row structure.

Other options:

SKIP_BLANK_LINEScontrols handling of empty rows, not headers.

Snowflake does not auto-generate column names from header rows; table schemas must be predefined.

Including header rows as data is undesirable and incorrect in most pipelines.

Thus, SKIP_HEADER is used exclusively to omit header rows during ingestion.

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Custom roles in Snowflake allow administrators to design access patterns aligned with organizational security requirements. They are created by Snowflake administrators and do not have predefined privileges; instead, privileges must be granted explicitly. Custom roles can be assigned to users or other roles, just like system-defined roles. Thus, the key attributes are that they are created by administrators and tailored to meet specific organizational needs.

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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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Zero-Copy Cloning is ametadata-only operation, creating instantaneous clones without copying underlying data. Clones are fully writable and operate independently. Snowflake only allocates storage when data diverges due to modifications. It does not require significant storage nor duplicate physical data.

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Snowflake Notebooks provide native integration with Streamlit, allowing users to build interactive data apps directly inside Snowflake. Users can call Streamlit functions such as st.write(), st.dataframe(), st.button(), and st.slider() within Python cells. No external HTML, scripting, or network configuration is required. Streamlit components execute seamlessly in the notebook environment, enabling dashboards and interactive visualizations on top of Snowflake data.

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Snowflake Materialized Views physically store the results of a defined query. This pre-computation significantly improves performance for repetitive or complex queries, especially those based on aggregations and joins. Snowflake automatically maintains materialized views asynchronously. They do not update in real-time, cannot be changed through DML, and are not intended for masking—dynamic masking policies serve that purpose.

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Snowflake provides a predefined set ofsystem-defined rolesthat enforce Role-Based Access Control (RBAC). These roles ensure structured governance and centralized privilege management across accounts. The primary system-defined roles include:

ACCOUNTADMIN, the highest-privileged role, responsible for global account-level activities such as billing, governance, replication, and cross-region/cloud configuration. It has implicit ownership of all objects.

SECURITYADMINmanages users, roles, MFA, and all privilege grants. This role ensures operational control over user lifecycle management while supporting separation of duties from ACCOUNTADMIN.

SYSADMINmanages objects such as databases, schemas, tables, warehouses, and other compute objects. It is the default role for data engineering and data platform teams needing full control of object creation and maintenance.

DATABASEADMINdoes not exist as a system-defined role—it is typically user-created for customization. System roles form a foundational security model for controlled privilege escalation and governance.

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A virtual warehouse is the compute engine of Snowflake. It provides CPU, memory, and temporary storage needed to execute SQL queries, data loading operations, and DML actions. Warehouses can be sized dynamically and suspended or resumed to optimize cost.

Warehouses donotstore data; Snowflake’s storage is independent and centralized. Warehouses do not manage roles—access control is handled through Snowflake’s RBAC system. Encryption is performed automatically by Snowflake’s storage and cloud services, not by warehouses.

Thus, the correct characteristic is that virtual warehouses supply compute.

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TheLIMITclause is used to restrict the number of rows returned by a query. The correct syntax in Snowflake is:

SELECT * FROM employees LIMIT 10;

This retrieves the first 10 rows of the result set. LIMIT appears at the end of the SELECT statement. Snowflake follows a SQL syntax similar to MySQL/PostgreSQL regarding LIMIT usage.

Incorrect forms include:

Using LIMIT before SELECT

Embedding LIMIT inside WHERE

Reversing the order of SELECT and LIMIT

LIMIT is frequently used for sampling, record previewing, debugging, and exploratory analysis.

Snowflake supports two official stage types:

• Internal Stages – User stages, table stages, and named internal stages stored inside Snowflake.

• External Stages – Refer to cloud storage (S3, Azure Blob, GCS) using integration credentials.

Managed, permanent, and temporary stages are not official Snowflake classifications.

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