SOL-C01 Snowflake SnowPro Associate: Platform Certification Exam Free Practice Exam Questions (2025 Updated)

Snowflake’s object hierarchy begins with theaccount, which contains one or moredatabases. Each database can contain multipleschemas, and each schema contains objects such astables,views,stages, and functions. The schema is a logical boundary for grouping related database objects. A schema cannot contain databases, and tables cannot contain schemas. Instead, tables and views are contained within schemas. Therefore, the two correct hierarchical relationships are that a database can contain multiple schemas, and a schema can contain multiple tables and views.

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TheACCOUNTADMINrole is Snowflake’s highest-privileged system-defined role. It provides complete administrative authority across the entire Snowflake account. Its functions include:

Managing global account parameters, replication settings, business continuity, failover groups, and region configurations

Administering billing, resource monitoring, and governance

Granting and revoking privileges across all objects and roles

Overseeing role hierarchy, including SECURITYADMIN and SYSADMIN

It is typically reserved for platform owners and security/governance teams, following least-privilege principles.

“Create and manage databases” is primarily a SYSADMIN responsibility.

“Monitor query performance” can be accomplished by roles with MONITOR privileges; it is not exclusive to ACCOUNTADMIN.

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ADatabaseis the highest logical container for schemas and their objects in Snowflake.

Hierarchy:

Account → Database → Schema → Tables, Views, Stages, etc.

Schemas organize objects within a database; tables and views do not contain objects.

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Changing a Snowflake virtual warehouse from Medium to Large increases the compute resources allocated to that warehouse—such as CPU cores, memory, and I/O bandwidth. This process is calledscaling up, or vertical scaling. It is typically performed to improve performance for complex queries, large joins, or computationally heavy workloads.

Scaling down is the reverse—reducing warehouse size from Large to Medium or similar.

Scaling out refers to adding more clusters to a multi-cluster warehouse, enabling increased concurrency rather than increasing power per cluster.

Scaling in is the reverse of scaling out—reducing the number of active clusters.

Thus, increasing the size from Medium to Large is vertical scaling, also known as scaling up.

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Snowflake allows users to list files in a stage using the LIST command, and it provides a shorthand alias: LS. Both commands return identical results, including file name, size, last modified timestamp, and other metadata stored in the stage. This is relevant for internal stages (user, table, named stages) and external stages backed by cloud storage. SHOW and DESC are unrelated commands—SHOW lists Snowflake metadata objects such as tables, schemas, or roles, while DESC describes object structures. “LIS” is not a Snowflake command. Therefore, LS is the exact shorthand synonym for LIST and is used identically in queries.

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A red checkmark in Snowflake Notebooks indicates that the cell encountered anerrorduring execution. This visual indicator helps users quickly identify issues so they can inspect the error message and adjust code accordingly. Successful execution shows a green checkmark, while running cells show a spinner. Snowflake notebooks do not include a “paused” state for notebook cells.

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Roles gain access to objects in Snowflake throughdirect privilege grantsor throughinheritancewithin a role hierarchy. A role may be explicitly granted privileges such as SELECT, OWNERSHIP, or USAGE. Alternatively, if a role is assigned to another role, it inherits all privileges of its parent role. Neither network settings nor data storage processes determine role access.

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TheVARIANTdata type stores semi-structured data (JSON, Parquet, XML, Avro, ORC).

VARIANT allows schema-on-read and supports nested structures.

TEXT/VARCHAR/STRING store plain text only and cannot efficiently query nested structures. STRING is not a Snowflake-native type—VARCHAR is used instead.

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Scalingup or downrefers tovertical scaling, meaning the warehouse’s compute size is increased or decreased. For example, moving fromSmall → Medium → Largeincreases CPU, memory, and I/O capacity, enabling faster processing for compute-intensive workloads.

Vertical scaling improves single-query performance, large ETL jobs, complex joins, or transformations. It does not improve concurrency unless multi-cluster mode is also used.

Horizontal scaling (scaling out/in), by contrast, adjusts thenumber of clustersand is used for concurrency.

Region selection is fixed at account creation and cannot be changed by resizing a warehouse. Storage movement is unrelated to compute rescaling.

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Snowflake is best described as anAI Data Cloud Platform. While Snowflake originated as a cloud data warehouse, its capabilities have expanded to include secure data sharing, application development, machine learning workloads, unstructured data processing, Snowpark, and integrated LLM/AI functions (such as Snowflake Cortex and Streamlit). This broad ecosystem supports analytic workloads, governance, multi-cloud architecture, and AI-driven applications. It is not simply an ETL tool or a transactional database.

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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 Notebooks support multiple cell types for development and documentation.Pythoncells allow users to perform data transformations, analysis, and Snowpark operations.SQLcells enable executing Snowflake SQL queries directly against tables and views.Markdowncells support formatted documentation within notebooks—useful for narrative explanations and commentary. The option “Markup” is not a supported cell type in Snowflake Notebooks. These three supported types allow users to blend analytics, code execution, and documentation in a unified environment.

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TheAccountis the top-level Snowflake container encompassing:

• All databases

• All schemas

• All compute resources (warehouses)

• All roles, users, and governance structures

All other objects exist within the Account context.

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Scaling up a warehouse increases compute resources (CPU, memory), improving performance on heavy workloads: large joins, transformations, and long-running analytical queries.

It does not increase storage, reduce cost, or guarantee higher concurrency (multi-cluster handles concurrency).

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Auto-scale enables Snowflake to dynamically start or stop clusters in a multi-cluster warehouse depending on workload pressure. When concurrency rises, Snowflake adds clusters; when demand decreases, it scales back down. This provides seamless performance during peak workloads and efficient resource usage during off-peak times. Auto-scale does not require manual action—it is fully automated once configured. It is not limited to standard warehouses; multi-cluster mode is available in Enterprise Edition and above. Ultimately, auto-scale ensures elasticity and cost-efficient compute allocation.

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Role hierarchy enables a parent role to inherit privileges assigned to child roles. This simplifies RBAC administration, reduces redundant grants, and ensures scalable security management. It has no relation to SQL sequencing, user categorization, or compute sizing.

TheCloud Services Layeris responsible for Snowflake’s global security, coordination, and metadata management. It orchestrates all authentication and authorization processes, including username/password login, MFA, OAuth, SSO, key-pair authentication, and integration with enterprise identity providers. It also manages role-based access control (RBAC), privilege enforcement, session management, and secure query orchestration.

This layer centralizes encryption policy enforcement, including the management of Snowflake-managed and customer-managed encryption keys (Tri-Secret Secure). While all data stored and transmitted in Snowflake is encrypted by default, it is the Cloud Services Layer that handles the key lifecycle, rotation, and hierarchy.

The Compute Layer only runs queries.

The Storage Layer keeps compressed, encrypted columnar data but does not manage security controls.

The Query Processing Layer executes SQL but does not manage authentication or RBAC.

Thus, the Cloud Services Layer is the authoritative control plane for Snowflake security.

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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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When a LIMIT clause is applied without an ORDER BY, Snowflake does not guarantee which rows will be returned. This means the result of:

SELECT count FROM my_table LIMIT 10;

is anon-deterministic set of 10 rows. Because Snowflake stores data in micro-partitions and distributes processing across compute resources, the natural order of rows is undefined unless explicitly controlled.

To retrieve the lowest or highest values, users must specify ORDER BY count ASC or ORDER BY count DESC respectively. Without ordering, Snowflake simply returnsany10 qualifying rows.

• Option B (highest values) requires ORDER BY count DESC.

• Option D (lowest values) requires ORDER BY count ASC.

• Option C (values ≤ 10) requires a WHERE clause (WHERE count <= 10).

Therefore, the query returns a non-predictable sample of 10 rows from the column, making option A correct.

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