Free Practice Questions for Snowflake SOL-C01 Exam

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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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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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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Snowflake is a fully cloud-native platform that supports deployment across all three major cloud providers:AWS, GCP, and Azure. Snowflake delivers consistent functionality across each provider, allowing organizations to choose the cloud best suited for regulatory, architectural, and business requirements. Although small feature differences may exist between cloud providers, Snowflake’s core capabilities—virtual warehouses, storage layers, security, data sharing, and governance—operate uniformly across all three platforms. This multi-cloud support gives Snowflake strong flexibility for hybrid, multi-region, and multi-cloud deployments.

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A multi-cluster warehouse raises Snowflake’s capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness. Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs. Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.

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Snowflake includes severalsystem-defined RBAC rolesto provide structured access control across the platform.

ACCOUNTADMINis the highest-privileged role, governing the entire account, including billing, replication, failover groups, and object ownership inheritance.

SECURITYADMINis responsible for user, role, and privilege management, including granting and revoking roles, password policies, and MFA enforcement. It is essential for identity lifecycle governance.

USERADMINmanages user creation, role creation, and user authentication administration. It is commonly assigned to identity administrators.

CUSTOM_ROLEis not a Snowflake system role; it is a user-created role, allowing organizations to design fine-grained, domain-specific access policies.

These predefined roles form the foundation of Snowflake’s security model and support least-privilege implementations.

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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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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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Snowsight’s Query History view allows filtering queries bystatus,date range, andexecuting user. Additional filters include SQL text search, warehouse, duration, statement type, session ID, query ID, query tag, parameterized query hash, and whether a query was generated by a client or task. These filters help users identify long-running, failed, queued, or specific types of queries for troubleshooting and performance optimization. Network or hardware configurations have no role in Query History filtering.

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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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