SOL-C01 Exam Results

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.

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

Internal stages are fullySnowflake-managed storage locationsused to temporarily store data files for loading or unloading operations. Examples include:

User stages (@~)

Table stages (@%table_name)

Named internal stages (@my_internal_stage)

Because they resideinside Snowflake, users do not need AWS, Azure, or GCP credentials to access them. Internal stages support all file types Snowflake can process, including CSV, JSON, Parquet, Avro, ORC, images, PDFs, and other semi-structured formats.

Incorrect statements:

They arenot limitedto structured or semi-structured data—they accept all supported formats.

External cloud credentials are only needed for external stages (S3, Blob, GCS).

Internal stages simplify secure data ingestion and operational pipelines because Snowflake handles encryption, metadata, and lifecycle management.

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

Streamlit inside Snowflake Notebooks enables users to buildinteractive, real-time visualizations and data applicationsdirectly within Snowsight. Because Streamlit is event-driven, users can create UI components such as sliders, dropdowns, date pickers, buttons, and filters that dynamically update charts, tables, and computed outputs.

Data remains inside Snowflake, processed through Python with Snowpark, eliminating data movement to external systems. This allows rapid prototyping of dashboards, analytical workflows, and AI-driven apps. Streamlit also supports layout customization, reactive components, and seamless integration with Snowflake compute for scalable analytics.

Incorrect options:

Streamlit doesnotmanage data storage.

Streamlit does not simplify code execution; it enhances visualization and interactivity.

Warehouse clusters are not fixed-size—Snowflake warehouses can scale.

Thus, its primary benefit is enablingrich interactive analytics directly within Snowflake.

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

Snowflake compute resources are referred to asVirtual Warehouses. A virtual warehouse is a cluster of compute nodes that executes SQL queries, performs DML operations (INSERT/UPDATE/DELETE), and runs data loading or transformation tasks.

Virtual Warehouses provide:

Dedicated compute isolation

Independent scaling (resize at any time)

Concurrency support through multi-cluster mode

Auto-suspend and auto-resume for cost efficiency

While Virtual Warehouses consist of compute clusters under the hood, Snowflake abstracts the underlying VM and node architecture, exposing only the warehouse construct to users. This ensures simplicity and avoids operational burdens such as node management.

Incorrect terms like Data Nodes or Virtual Machines represent underlying infrastructure concepts not exposed to end users.

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