Free Snowflake SOL-C01 Practice Exam with Questions & Answers

The Cloud Services Layer is the coordination and control layer of Snowflake’s architecture. One of its primary responsibilities is managing security, metadata, authentication, and system-wide services. This layer handles user authentication, role-based access control, metadata services (such as table structures, micro-partition metadata, statistics), query parsing, optimization, execution coordination, and transaction management.

It does not store customer data; storage is handled by the Database Storage Layer using micro-partitions. It does not manage virtual warehouses directly; warehouses are part of the Compute Layer. While Snowsight is a UI that interacts with the Cloud Services Layer, the interface itself is not part of the architectural layer.

The Cloud Services Layer essentially acts as the “brain” of Snowflake, ensuring the platform is consistent, secure, optimized, and able to scale operations intelligently across compute clusters and cloud-native storage environments.

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Snowflake Notebooks provide an interactive environment where developers and analysts can combineSQL,Python, visualization libraries, and documentation. Use cases include exploratory analysis, ETL prototyping, data engineering workflows, machine learning model development, and dashboard creation using Streamlit components.

Notebooks allow mixing of SQL and Python cells with shared session state, enabling smooth transitions between Snowpark DataFrames, SQL queries, visual charts, and markdown explanations. They enhance reproducibility and collaboration, supporting versioning, parameterization, and seamless Snowflake compute integration.

Incorrect options:

Raw data is stored in Snowflake tables, not in notebooks.

User authentication is handled by the Cloud Services Layer.

Managing cloud storage is automatic and not a notebook responsibility.

Thus, Snowflake notebooks are an end-to-end development and analytics interface.

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Snowsight includes a modern, fully capableSQL editordesigned for rich query development, execution, and analysis. The editor offers features such as syntax highlighting, intelligent auto-complete, contextual error display, and result-set visualization. Users can organize work with worksheets, folders, tagging, and versioning-like behavior.

Snowsight is built for both simple and complex SQL workloads. It supports multi-statement execution, script-like workflows, stored procedure development, materialized views, and advanced analytics queries.

Unsupported options include:

Manual data storage configuration: Snowflake abstracts storage management completely.

Pre-built queries only: Snowsight allows fully custom SQL.

Limited to simple queries: It supports enterprise workloads, BI query chains, and operational SQL.

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Snowflake provides intuitive mechanisms to navigate semi-structured data stored in VARIANT columns, especially JSON, XML, Parquet, and Avro. Two primary access methods aredot notationandbracket notation. Dot notation, such as column.nested.key, allows straightforward access to nested elements using a hierarchical reference. Bracket notation, such as column['key name'], allows access to elements with special characters, spaces, or numeric array indices. While Snowflake also offers powerful functions such as FLATTEN(), OBJECT_CONSTRUCT(), and ARRAY_LENGTH(), these operate on semi-structured types but do not serve as direct field-access mechanisms. Cloud Functions play no role in querying semi-structured fields inside Snowflake. Dot and bracket notation work together to provide full expressiveness, enabling users to traverse complex hierarchical structures directly in SQL, making Snowflake an ideal platform for semi-structured data analytics.

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Snowflake’sTime Travelenables querying historical data as it existed at a prior point in time. This is accomplished using theATorBEFOREclause within the FROM statement:

SELECT * FROM my_table AT (TIMESTAMP => '2025-06-18 10:00:00');

SELECT * FROM my_table BEFORE (OFFSET => -3600);

Time Travel supports access via timestamp, statement ID, or time offset. This is essential for auditing, recovery, and reconstructing past states for analysis.

Incorrect options:

RESTOREis used to recover dropped objects, not to query history.

BACKUPis not a Snowflake SQL clause.

HISTORYis not a valid SQL clause.

Thus, AT | BEFORE enables precise historical queries.

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Snowsight is the default web-based interface for Snowflake. It provides a graphical, browser-based environment to write and run SQL and Python, manage and explore database objects, create visualizations and dashboards, monitor query and warehouse activity, and collaborate via worksheets. It has replaced the Classic Console as the default UI for new Snowflake accounts.

SnowSQL is a command-line client used to connect to Snowflake from a terminal and execute SQL statements. Snowpark is a developer framework that supports writing data applications in languages like Python, Java, and Scala, but it is not a UI. “Snowflake CLI” refers to command-line tooling and automation utilities, again not a graphical web interface.

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USERADMIN is one of Snowflake’ssystem-defined roles, created automatically in every account. It is responsible for managing users and roles, including CREATE USER, ALTER USER, and role assignment. It is part of Snowflake’s default RBAC hierarchy (SYSADMIN, SECURITYADMIN, USERADMIN, etc.).

SNOWFLAKE_ADMIN and SNOWFLAKE_DBA are not Snowflake system roles—they may exist in organizations as custom roles but do not appear by default. DATA_ENGINEER is also user-created and not a built-in role.

Therefore, USERADMIN is the only true system-defined role listed.

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LIST @stage_name displays all files present in a stage (internal or external).

PUT uploads local files to an internal stage.

GET downloads files from an internal stage.

COPY INTO loads or unloads data but does not list files.

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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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Snowflake provides two main types of stages:Internal stagesandExternal stages. Internal stages reside inside Snowflake and include user stages, table stages, and named internal stages. These offer convenient, secure storage within Snowflake’s managed environment. External stages reference cloud storage locations such as AWS S3, Azure Blob Storage, or Google Cloud Storage, enabling integration with external data repositories. Snowflake does not define a stage type called “Public” or “Private,” although stages can be granted secure access. Thus, the correct types are Internal and External stages.