Snowflake SnowPro Core Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Snowflake AI Data Cloud Features and Architecture (24% of the exam)

1.1 Outline key features of the Snowflake AI Data Cloud.

• Interoperable storage
• Elastic compute
• Snowflake’s layers
• Overview of Snowflake editions

1.1 summary: This section introduces the fundamental building blocks that define the Snowflake AI Data Cloud. You will learn how Snowflake’s cloud-agnostic approach supports interoperable storage and elastic compute, allowing seamless data management across multiple regions and platforms. The focus is on understanding how Snowflake delivers scalability, reliability, and unified access without the burden of infrastructure management.

You will also explore Snowflake’s unique multi-layer architecture and the differentiating features among Snowflake editions. By grasping how storage, compute, and cloud services layers interact, you can explain how Snowflake provides performance isolation, simplified governance, and cost-effective data collaboration that adapts to any workload.

1.2 Outline key Snowflake tools and user interfaces.

• Snowsight
• SnowSQL
• Snowflake connectors
• Snowflake drivers
• Snowpark
• SnowCD
• Streamlit in Snowflake
• Cortex (AI/ML services)
• Snowflake SQL API

1.2 summary: This section highlights the variety of interfaces and tools available for working efficiently in Snowflake. You'll explore Snowsight for intuitive administration and visualization, SnowSQL for command line interaction, and integration capabilities through connectors, drivers, and APIs. The goal is to understand how each option caters to different workflows, from data loading to automation.

You’ll also learn how to extend functionality using Snowpark for programmatic data transformations and Streamlit in Snowflake for building interactive data apps. This section emphasizes the breadth of Snowflake’s ecosystem, showing how both developers and analysts can achieve flexibility, extensibility, and innovation within the same unified cloud environment.

1.3 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

1.3 summary: This section explores the building blocks of Snowflake’s data organization and management model. You’ll review how databases, schemas, and tables structure your datasets, as well as how views, stages, and shares facilitate collaboration and data movement. Understanding these objects helps ensure consistent storage, governance, and access control practices across the platform.

Additionally, you will examine programmable objects like UDFs, stored procedures, tasks, and streams that enable dynamic and automated data processes. The emphasis is on understanding how these features combine to create modular, maintainable, and reusable workflows for end-to-end data operations.

1.4 Outline Snowflake storage concepts.

• Micro-partitions
• Data clustering
• Data storage monitoring

1.4 summary: This section focuses on Snowflake’s unique storage optimization techniques. You’ll discover how micro-partitions organize and automatically maintain data for optimal performance and how clustering improves query efficiency by minimizing the amount of data scanned.

Additionally, you’ll learn how to use data storage monitoring tools to keep visibility over consumption and efficiency. By understanding these underlying mechanics, you can confidently apply storage strategies that sustain performance, transparency, and balance between compute cost and speed.

Domain 2: Account Access and Security (18% of the exam)

2.1 Outline security principles.

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

2.1 summary: This section establishes the essential pillars of Snowflake account security. You’ll examine how Snowflake uses MFA, key pairs, and federated authentication to protect access, while network policies provide granular control over connections. The goal is to understand how these mechanisms combine to safeguard data environments at every layer.

You’ll also explore SSO configurations and identity federation to streamline secure access across corporate authentication systems. By mastering these foundations, you’ll gain the knowledge to design an environment that balances convenience and compliance without compromising data protection.

2.2 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

2.2 summary: This section dives into the access control model at the heart of Snowflake security. You will learn about roles, privileges, and how inheritance defines who can perform specific operations. By understanding how to grant or revoke privileges, you’ll ensure users operate according to the principle of least privilege.

You’ll also see how hierarchical roles streamline governance across complex organizations, enabling scalable and traceable administration. The focus is on creating a structured authorization system that supports accountability and audit readiness.

2.3 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

2.3 summary: This section demonstrates how Snowflake supports data security and regulatory compliance through governance capabilities. You’ll explore account and organizational structures that enable enterprise-level oversight, along with secure views and functions that protect sensitive data.

Further, you will examine information schemas, access history, and tagging methods to document, classify, and monitor access patterns. The emphasis is on developing transparent governance processes that strengthen accountability and ensure that data access aligns with organizational policies.

Domain 3: Performance and Cost Optimization Concepts (16% of the exam)

3.1 Explain the use of the Query Profile.

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

3.1 summary: This section focuses on evaluating query performance with Snowflake’s Query Profile. You’ll learn how to interpret query execution plans, identify opportunities to reduce data scans through micro-partition pruning, and manage performance via caching.

You’ll also gain insights into detecting data spills and leveraging query history for diagnostic insights. By applying Query Profile effectively, you can improve response times and deliver optimized performance that aligns with workload demands.

3.2 Explain virtual warehouse configurations.

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

3.2 summary: This section covers warehouse configuration in Snowflake, which directly affects performance and cost. You’ll explore various warehouse types and how their size and scaling behavior influence concurrency and efficiency.

Key topics include scaling policies, modes, and best practices for adjusting compute according to workload patterns. You’ll develop the ability to choose the right configuration that balances responsiveness, resource utilization, and spend optimization.

3.3 Outline virtual warehouse performance tools.

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

3.3 summary: This section explains Snowflake’s built-in tools for ongoing warehouse performance management. You’ll learn how to analyze loads and performance metrics in real time to maintain throughput and consistency.

You’ll also explore scaling strategies and how the Query Acceleration Service supports faster performance under demanding workloads. The key takeaway is knowing which options to use to keep compute resources efficient and cost-aware.

3.4 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

3.4 summary: This section focuses on strategies for accelerating queries through smart design choices and caching mechanisms. You’ll learn how to implement materialized views and clustering effectively to reduce execution time and data scans.

Additionally, you will explore caching layers and Snowflake’s search optimization service to improve performance for both ad hoc and repetitive workloads. The emphasis is on combining techniques for sustained query responsiveness and efficiency.

3.5 Describe cost optimization concepts and best practices in Snowflake.

• Understanding and exploring the costs of different Snowflake features and services — Cost insights feature in Snowsight
• Understanding and exploring the costs of different Snowflake features and services — Use of different table types and sizes
• Understanding and exploring the costs of different Snowflake features and services — Use of views
• Understanding and exploring the costs of different Snowflake features and services — Use of search optimization paths
• Understanding and exploring the costs of different Snowflake features and services — Storage costs
• Understanding and exploring the costs of different Snowflake features and services — Compute costs
• Understand and explore cloud services costs in Snowflake
• Costs considerations when using serverless features
• Cost considerations when moving data among regions — Replication
• Cost considerations when moving data among regions — Fail-over
• Monitor and control costs — Resource monitors
• Monitor and control costs — Snowflake Budgets service
• Attribute costs — Cost center tagging
• Attribute costs — Use of the ACCOUNT_USAGE schema

3.5 summary: This section highlights the practices for balancing performance with cost in Snowflake. You’ll use features like Snowsight cost insights and budgets to identify usage patterns and optimize expenditure across compute, storage, and services.

You’ll also learn about strategies for controlling costs through tagging, monitoring, and managing multi-region operations. The focus is on understanding transparency and accountability to achieve efficient, scalable, and sustainable Snowflake environments.

Domain 4: Data Loading and Unloading (12% of the exam)

4.1 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

4.1 summary: This section introduces the processes and decisions involved in loading data into Snowflake. You’ll learn about internal and external stages, suitability of file formats, and how folder structures support organized pipelines.

It also explains ad hoc versus automated loading patterns, highlighting how Snowpipe enables continuous ingestion. By mastering these concepts, you’ll improve data availability, reliability, and alignment with performance goals.

4.2 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

4.2 summary: This section details the commands that bring data from files into Snowflake. You’ll examine how CREATE and COPY commands define structures and manage file-to-table ingestion for diverse workflows.

Practical understanding of staged file handling, data validation, and external table setup allows you to design optimized pipelines. The section connects the syntax with real-world use cases, reinforcing choice and efficiency in ingestion design.

4.3 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

4.3 summary: This section explores methods for exporting Snowflake data efficiently. You’ll learn how to choose formats, compress output, and handle nuances like empty strings or NULLs to maintain data integrity.

You’ll also review unloading into single or multiple files, identifying the trade-offs of each method for post-processing. The focus is on maintaining flexibility, performance, and compatibility with downstream systems.

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

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

4.4 summary: This section introduces the fundamental commands for extracting data from Snowflake. You’ll explore how to generate file exports using COPY INTO and manage them using GET and LIST commands.

Understanding when and how to stage and format files ensures accurate delivery to external systems. The section provides clarity on export automation and adaptability for enterprise data workflows.

Domain 5: Data Transformations (18% of the exam)

5.1 Explain how to work with structured data.

• Estimation functions
• Sampling — SAMPLE command
• Sampling — Use /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

5.1 summary: This section focuses on structured data handling within Snowflake using SQL and procedural tools. You’ll learn about sampling, estimation, and function types for efficient analysis and automation.

You’ll also explore how streams and tasks manage change data capture and scheduling for continuous transformations. The goal is to empower you with approaches that simplify and scale everyday data engineering workflows.

5.2 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

5.2 summary: This section covers managing and transforming semi-structured data like JSON, Avro, or Parquet within Snowflake. You’ll use VARIANT columns and specialized SQL functions to handle schema flexibility efficiently.

The emphasis includes flattening nested data and extracting key attributes using flexible query methods. This enables analysts to work seamlessly with complex datasets without extensive pipeline redesign.

5.3 Explain how to work with unstructured data.

• Define and use directory tables
• SQL file functions
• Types of URLs used to access data files
• Processing unstructured data — User-Defined Functions (UDFs) for unstructured data analysis
• Processing unstructured data — Stored procedure

5.3 summary: This section introduces Snowflake’s approach to unstructured data such as images, audio, and documents. You’ll learn to use directory tables and SQL file functions to query and manage this content in one unified platform.

You’ll also understand how UDFs and stored procedures enable custom processing challenges for diverse data types. Together, these capabilities illustrate how Snowflake integrates structured, semi-structured, and unstructured data seamlessly.

Domain 6: Data Protection and Data Sharing (12% of the exam)

6.1 Outline Continuous Data Protection with Snowflake.

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

6.1 summary: This section centers on Snowflake’s robust data protection features that guard availability and integrity. Learn how Time Travel and Fail-safe leverage automated backups to restore historical states or recover after events.

You’ll also understand encryption, cloning, and replication mechanisms that ensure secure redundancy and resilience across regions. The focus is on applying continuous data protection practices that meet enterprise recovery objectives.

6.2 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 — Direct shares
• Secure Data Sharing — Data listings

6.2 summary: This section discusses how Snowflake democratizes data collaboration through secure sharing frameworks. You’ll explore direct shares, listings, and the use of the Snowflake Marketplace and Data Exchange to distribute data responsibly.

You’ll also learn to manage privileges and use DDL-based controls for consistent, secure access governance. The focus is on creating connected ecosystems where partners and teams can exchange insights instantly with trust and precision.