Snowflake SnowPro Core Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Snowflake Data Cloud Features and Architecture (25% of the exam)

Outline key features of the Snowflake Data Cloud.

• Elastic storage
• Elastic compute
• Snowflake’s three distinct layers
• Cloud partner categories
• Overview of Snowflake editions

Summary: This section introduces the foundational characteristics that make Snowflake unique as a data platform. Elastic storage and elastic compute are central principles, enabling seamless scalability that adjusts to workload demands. The architecture is organized into three core layers that define its separation of storage, compute, and services — a design that empowers organizations to scale independently and efficiently. Additionally, the partnerships within Snowflake’s ecosystem and the differences between its editions give you context for how Snowflake integrates into broader enterprise strategies.

Understanding these elements helps tie technology back to business value. You will learn not only what the architecture entails but also why this independence and scalability translate into cost efficiency, high performance, and flexibility for enterprises. The ability to articulate these benefits is essential for proving your readiness to adopt and optimize Snowflake within diverse environments.

Outline key Snowflake tools and user interfaces.

• Snowsight
• SnowSQL
• Snowflake connectors
• Snowflake drivers
• Snowpark
• SnowCD

Summary: This section introduces the interfaces and tools that allow users to interact with Snowflake across different use cases. Snowsight provides a web interface for queries, dashboards, and administrative tasks, while SnowSQL is the command-line client for performing similar operations. Connectors and drivers extend functionality into data pipelines, programming languages, and BI tools. More advanced integrations come through Snowpark for building applications within a familiar development environment, and SnowCD for deployment automation.

By learning these tools, you’ll be able to choose the right interface for the right task. For example, data analysts might lean towards Snowsight, developers toward Snowpark, and administrators toward SnowSQL or deployment tools. Success lies in understanding how these tools complement one another and when to leverage each for efficient collaboration within projects.

Outline Snowflake’s catalog and objects.

• Databases
• Stages
• Schema types
• Table types
• View types
• Data types
• User-Defined Functions (UDFs)
• User Defined Table Functions (UDTFs)
• Stored procedures
• Streams
• Tasks
• Pipes
• Shares
• Sequences

Summary: This section emphasizes Snowflake’s data catalog and its variety of objects for managing and transforming data. Understanding how databases, schemas, tables, and views are organized provides the structural foundation for everything you build. Beyond simple storage, the objects extend into functions, stored procedures, and automation tools like tasks and pipes that enable continuous workflows.

A clear picture of these resources is key to designing efficient and organized environments. You will gain fluency in defining and orchestrating assets like streams for change tracking or shares for collaboration, while also applying appropriate object types based on the kind of workloads and data structures used. Practical knowledge in these areas ensures you can implement Snowflake solutions that scale and integrate smoothly.

Outline Snowflake storage concepts.

• Micro-partitions
• Data clustering
• Data storage monitoring

Summary: This section explains how Snowflake physically stores data behind the scenes. Data is automatically split into micro-partitions, which improves efficiency in storage and query performance. Clustering adds further optimization by grouping related data, while storage monitoring helps you track consumption and ensure resources are managed properly.

A deeper appreciation of these ideas helps you link performance optimizations to architecture design. By understanding data partitioning and clustering strategies, you will recognize how Snowflake delivers high performance with minimal intervention. This leads to better query efficiency, faster response times, and cost-conscious designs that appeal to both engineers and decision-makers.

Domain 2: Account Access and Security (20% of the exam)

Outline security principles.

• Network security and policies
• Multi-Factor Authentication (MFA)
• Federated authentication
• Key pair authentication
• Single Sign-On (SSO)

Summary: This section focuses on the security measures available to protect accounts in Snowflake. You’ll see how factors like network policies and key authentication methods ensure secure access. MFA and SSO support modern methods of identity protection, while federated authentication and key pair measures allow adaptable approaches for enterprise systems.

These principles illustrate Snowflake’s commitment to safeguarding data at every level. By adopting them, organizations not only ensure compliance but also increase user trust. You will develop a practical understanding of aligning authentication to business needs with both convenience and security in mind.

Define the entities and roles that are used in Snowflake.

• Overview of access control — Access control frameworks
• Overview of access control — Access control privileges
• Outline how privileges can be granted and revoked
• Explain role hierarchy and privilege inheritance

Summary: This section introduces Snowflake’s role-based access control model. You’ll learn about frameworks, how privileges can be assigned, and the way hierarchy creates structured access across organizations. Privileges determine what actions users can take, and inheritance concepts simplify scaling security across larger teams.

The role model ties together clarity, governance, and efficiency. It allows administrators to implement secure policies without creating complexity for end users. By understanding this hierarchy, you’ll be able to design systems that balance flexibility with control for teams of any size.

Outline data governance capabilities in Snowflake.

• Accounts
• Organizations
• Secure views
• Secure functions
• Information schemas
• Access history — Tracking read/write operations
• Overview of row/column-level security
• Object tags

Summary: This section explains the features Snowflake provides for strong data governance practices. Capabilities include secure views and functions for protecting sensitive data, row and column-level security for fine-grained control, and tagging options for classification and policy enforcement. Access history and robust account structures add visibility into who is accessing what.

These features allow organizations to align with compliance and auditing standards while maintaining productivity. Strong governance capabilities also highlight Snowflake’s value in large enterprises, where operational trust and transparency are critical. You’ll learn how governance features enable collaboration without reducing security.

Domain 3: Performance Concepts (15% of the exam)

Explain the use of the Query Profile.

• Explain plans
• Data spilling
• Use of the data cache
• Micro-partition pruning
• Query history

Summary: This section highlights how to investigate and interpret Snowflake query performance using the Query Profile. It shows how explain plans reveal execution steps and dependencies, while features like micro-partition pruning and caching improve speed. Tracking query history adds further visibility into performance trends.

A practical understanding of the Query Profile allows for confident tuning and optimization. By mastering this feature, you can ensure queries run efficiently while demonstrating skill in analyzing execution patterns. It reflects both proactive management and problem-solving ability in performance design.

Explain virtual warehouse configurations.

• Types of warehouses
• Multi-clustering warehouses — Scaling policies
• Multi-clustering warehouses — Scaling modes
• Warehouse sizing
• Warehouse settings and access

Summary: This section explores Snowflake’s compute resources known as warehouses. Different warehouse types can be tailored to workload sizes, with scaling policies and multi-clustering providing elasticity. Configuration options like size, mode, and access rights empower you to balance speed with cost control.

By mastering warehouse configuration choices, you will learn how to adjust compute resources to business priorities. Understanding when to scale up or out enhances optimization skills, ensuring you can architect environments that both perform and align with organizational budgets.

Outline virtual warehouse performance tools.

• Monitoring warehouse loads
• Scaling up compared to scaling out
• Resource monitors
• Query acceleration service

Summary: This section centers on monitoring and enhancing the performance of Snowflake compute resources. You’ll investigate how scaling methods address workloads, compare scaling up versus scaling out, and apply specialized options like the query acceleration service. Tools like resource monitors also support cost and usage control.

Through these tools, you will gain practical insight into balancing performance and efficiency. Effective use of monitoring ensures proactive management, while acceleration services illustrate Snowflake’s ability to handle growing demands with agility.

Optimize query performance.

• Describe the use of materialized views
• Use of specific SELECT commands
• Clustering
• Search optimization service
• Persisted query results
• Understanding the impact of different types of caching — Metadata cache
• Understanding the impact of different types of caching — Result cache
• Understanding the impact of different types of caching — Warehouse cache

Summary: This section emphasizes techniques to improve the speed and efficiency of queries in Snowflake. Tools like materialized views, search optimization, and clustering help minimize runtime complexity. Understanding persisted results and multiple layers of caching further enhances performance strategies.

By combining these options, you’ll learn to optimize workloads end-to-end in Snowflake. Linking efficient commands and services to caching mechanisms illustrates how thoughtful architecture improves overall productivity.

Domain 4: Data Loading and Unloading (10% of the exam)

Define concepts and best practices that should be considered when loading data.

• Stages and stage types
• File size and formats
• Folder structures
• Ad hoc/bulk loading
• Snowpipe

Summary: This section provides insights into how Snowflake manages data ingestion at scale. You’ll see how stages organize file storage, what file sizes and formats are ideal, and the place of Snowpipe in automating continuous data ingestion. Attention to folder structures ensures reliable architecture for repeatable loading.

This knowledge ensures efficient pipelines and reduces the risk of delayed insights. Loading practices highlight Snowflake’s flexibility and demonstrate your capacity to design ingestion strategies that align with business needs and growth.

Outline different commands used to load data and when they should be used.

• CREATE STAGE
• CREATE FILE FORMAT
• CREATE PIPE
• CREATE EXTERNAL TABLE
• COPY INTO
• INSERT/INSERT OVERWRITE
• PUT
• VALIDATE

Summary: This section focuses on commands essential to loading data. You’ll explore commands for staging files, describing formats, automating ingestion pipelines, and validating load processes. Commands like COPY INTO and INSERT provide multiple approaches for getting data into Snowflake systems.

Mastering these commands equips you with the technical precision needed for smooth loading operations. It empowers you to decide between techniques based on the data source, structure, and downstream application requirements.

Define concepts and best practices that should be considered when unloading data.

• File size and formats — Overview of compression methods
• Empty strings and NULL values
• Unloading to a single file
• Unloading relational tables

Summary: This section details strategies for exporting data out of Snowflake. Managing file sizes and formats, handling empty or null values, and applying techniques like compression help ensure unloading processes remain effective and performant. Special practices such as unloading to single files provide alternatives for specific needs.

Understanding these details ensures smooth data transfers to other environments. It enables you to align export operations with best practices in performance and interoperability, demonstrating versatile knowledge of Snowflake.

Outline the different commands used to unload data and when they should be used.

• GET
• LIST
• COPY INTO
• CREATE STAGE
• CREATE FILE FORMAT

Summary: This section covers the commands required to move data out of Snowflake environments. Tools like GET, LIST, COPY INTO, and others allow data to be retrieved seamlessly and combined with defined formats for external use.

By applying these commands, you will gain expertise in managing both inbound and outbound data operations. In doing so, you complete the cycle of Snowflake usage by showing proficiency in movement of information across different contexts.

Domain 5: Data Transformations (20% of the exam)

Explain how to work with standard data.

• Estimation functions
• Sampling — SAMPLE command
• Sampling — TABLESAMPLE command
• Sampling — Sampling methods — Fraction-based
• Sampling — Sampling methods — Fixed-size
• Supported function types — System functions
• Supported function types — Table functions
• Supported function types — External functions
• Supported function types — User-Defined Functions (UDFs)
• Stored procedures
• Streams
• Tasks

Summary: This section introduces native techniques for working with structured or standard data within Snowflake. You’ll study functions, sampling techniques, and the varied approaches possible through UDFs, stored procedures, and task automation. Tools like streams keep workloads updated and consistent.

Through these methods, you’ll see how data analysis gains depth and precision. Command of these transformations supports statistical work, workflow automation, and consistent processing across teams.

Explain how to work with semi-structured data.

• Supported data formats, data types, and sizes
• VARIANT column
• Flattening the nested structure — FLATTEN command
• Flattening the nested structure — LATERAL FLATTEN command
• Semi-structured data functions — ARRAY/OBJECT creation and manipulation
• Semi-structured data functions — Extracting values
• Semi-structured data functions — Type predicates

Summary: This section teaches how Snowflake natively manages semi-structured data types like JSON or Avro with the VARIANT column. You’ll utilize FLATTEN commands and related functions to unpack and make sense of complex nested structures. Supporting functions simplify analysis by enabling manipulation and extraction within data pipelines.

Semi-structured methods highlight one of Snowflake’s most versatile strengths. Being able to seamlessly integrate raw data alongside structured sources is transformative for analytics, and this section shows you how to achieve it.

Explain how to work with unstructured data.

• Define and use directory tables
• SQL file functions — Types of URLs available to access files
• Outline the purpose of User-Defined Functions (UDFs) for data analysis

Summary: This section highlights approaches for handling unstructured data in Snowflake. From directory tables to SQL file functions, Snowflake has capabilities for storing, accessing, and analyzing data from a variety of formats. UDFs add a layer of analytical power for applying business rules directly to this data.

These features make Snowflake adaptable to nearly any type of dataset, not just traditional tables. It empowers teams to combine structured, semi-structured, and unstructured sources under one scalable approach.

Domain 6: Data Protection and Data Sharing (10% of the exam)

Outline Continuous Data Protection with Snowflake.

• Time Travel
• Fail-safe
• Data encryption
• Cloning
• Replication

Summary: This section details Snowflake’s suite of data protection measures. Capabilities like Time Travel, fail-safe, and cloning ensure data reliability and recovery. Encryption further secures data, while replication adds resilience across geographies and accounts.

Mastering these measures ensures you can continuously safeguard critical information. Snowflake’s recovery and protection options support both compliance and uptime demands across enterprise operations.

Outline Snowflake data sharing capabilities.

• Account types
• Snowflake Marketplace
• Data Exchange
• Access control options — DDL commands to create and manage shares
• Access control options — Privileges required for working with shares
• Secure Data Sharing (for example, Direct Share, Listing)

Summary: This section demonstrates how Snowflake promotes collaboration through secure data sharing. You’ll review the types of accounts and learn how Data Exchanges and the Marketplace create opportunities for controlled partnerships. Privileges and DDL commands highlight how to grant proper access during sharing.

Data sharing reflects how organizations can use Snowflake to innovate across boundaries. The ability to share datasets securely functions as both a strategic and operational advantage in an increasingly data-driven world.