Snowflake SnowPro Specialty Snowpark Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Snowpark Concepts (15% of the exam)

Outline Snowpark architecture

• Lazy evaluation
• Use of key objects — Snowpark DataFrames
• Use of key objects — User-Defined Functions (UDFs)
• Use of key objects — User-Defined Table Functions (UDTFs)
• Use of key objects — Stored procedures
• Use of key objects — File operations
• Types of libraries (DataFrames, Machine Learning) — Anaconda repository (Python packages directly into Snowflake)
• Types of libraries (DataFrames, Machine Learning) — Other third-party libraries (not managed by Anaconda repository)
• Client-side and server-side capabilities

Summary: This section introduces Snowpark's overall architecture and shows how it aligns with the Snowflake environment. You will learn about Snowpark DataFrames, UDFs, UDTFs, stored procedures, and file operations as central building blocks. A strong grasp of these objects allows you to treat Snowpark as a flexible and powerful programming environment that extends Snowflake's functionality.

You will also explore how Snowpark integrates with libraries from both Anaconda and third-party providers. This section highlights the distinction between client-side and server-side operations, teaching you when it makes sense to process logic close to where the data lives. Understanding lazy evaluation and core architectural principles ensures you know how Snowpark achieves efficiency and adaptability for data workloads.

Set-up Snowpark

• Installation — Versioning
• Installation — Python environment
• Development environments — Third-party tools
• Development environments — Snowflake Notebooks
• Development environments — Jupyter Notebooks
• Development environments — Microsoft Visual Studio Code (VS Code)

Summary: This section explains the practical setup required to get started with Snowpark. You will become familiar with installation considerations, version choices, and Python environments that underpin development. The content clarifies how Snowpark can be integrated seamlessly into existing workflows while ensuring compatibility and stability for ongoing projects.

You will also see how different development environments play a role in productivity. Whether working directly in Snowflake Notebooks, using Jupyter for familiar workflows, or connecting through VS Code and other tools, this section equips you with the flexibility to choose an approach that matches your style. The focus is on enabling quick setup so your energy can go toward development and innovation.

Domain 2: Snowpark API for Python (30% of the exam)

Create and manage user sessions

• Account identifiers
• Parameters for the CONNECT function
• Authentication methods — Construct a dictionary
• Authentication methods — Key pair authentication
• Authentication methods — Snowflake CLI or .env parameters
• Session creation
• SessionBuilder
• Session methods
• Session attributes
• Asyncjob

Summary: This section explores how to create and manage Snowpark sessions, which form the foundation of interaction with the Snowflake environment. You will learn about authentication mechanisms, from key-pair methods to CLI-driven parameter setups, and see how these align with Snowflake security practices. Understanding session attributes and methods helps you gain direct control over how applications communicate with Snowflake.

By mastering tools like the SessionBuilder and working with async jobs, you will be prepared to manage long-running processes efficiently and securely. This part of the exam targets fluency with session connectivity and its role in enabling robust and scalable applications, ensuring you maintain best practices for both usability and governance.

Use Snowpark with unstructured data

• Read files with SnowflakeFile object
• Use UDFs and UDTFs to process files
• Use stored procedures to process files

Summary: This section demonstrates how Snowpark simplifies working with unstructured data. You will learn to utilize the SnowflakeFile object, enabling consistent access to files of different types for downstream processing. This empowers you to unify structured and unstructured datasets directly within Snowflake.

Beyond simple ingestion, you will see how UDFs, UDTFs, and stored procedures extend Snowpark for custom file handling. These tools allow you to bring logic directly to unstructured inputs, promoting a higher degree of integration and automation. The emphasis is on making unstructured data a seamless part of your analysis and applications rather than an external burden.

Create Snowpark DataFrames

• Multiple methods to create Snowpark DataFrames — From Snowflake tables/views
• Multiple methods to create Snowpark DataFrames — From Python objects (list, dictionary)
• Multiple methods to create Snowpark DataFrames — From SQL statements
• Multiple methods to create Snowpark DataFrames — From files (JSON, CSV, Parquet, XML)
• Multiple methods to create Snowpark DataFrames — From pandas DataFrames
• Schemas (apply to DataFrames)
• Data types (for example, IntegerType, StringType, DateType)

Summary: This section covers how Snowpark DataFrames act as the primary abstraction for data operations. You will discover multiple pathways to creating DataFrames, whether sourced from Snowflake tables, local structures like Python lists and dictionaries, or even inputs like files and pandas DataFrames. Each method has practical applications that allow teams to work with whatever data sources are most relevant.

By exploring schemas and data type handling, you will align your DataFrames with the exact requirements of downstream transformations. This ensures precision in analytics and data science workflows. A strong foundation in creating and tailoring DataFrames is essential to working efficiently with Snowpark and unlocking its power.

Operationalize UDFs and UDTFs in Snowpark

• Create UDFs from files (locally, on a stage)
• Use Python modules (packaged Python code) with UDFs
• Write Python function to create UDFs and UDTFs
• Register UDFs and UDTFs (for example, session.utf(...), functions.utf(...))
• Secure UDFs and UDTFs — Use SQL to alter UDFs and UDTFs created with Snowpark
• Secure UDFs and UDTFs — Grant access to UDFs and UDTFs to share code
• Secure UDFs and UDTFs — Understanding how to grant object permissions so other Snowflake users can see and use the UDFs and UDTFs
• Data types (type hints vs. registration API) — Provide the data types as parameters when creating a UDF or UDTF to return as Python hints/specify them as part of the registration
• Compare scalar and vectorized operations

Summary: This section highlights creating and operationalizing UDFs and UDTFs, giving you more dynamic ways to embed logic within Snowpark. You will learn how to register these objects, make use of Python modules, and define appropriate data types for return values. The focus is on aligning functional code with Snowflake's data processing strengths.

You will also see how to secure UDFs and UDTFs so they can be reused across teams responsibly. With principles like least privilege applied through object permissions, you enable collaborative development without sacrificing governance. The distinction between scalar and vectorized operations ties into performance optimization strategies covered later in the exam.

Operationalize Snowpark stored procedures

• Create stored procedures from files (locally, on stage)
• Write Python functions to power stored procedures
• Use Python modules (packaged code, Anaconda) with stored procedures
• Register stored procedures
• Make dependencies available to code
• Secure stored procedures — Use SQL to alter stored procedures created with Snowpark
• Secure stored procedures — Caller versus owner rights
• Use Snowpark Python stored procedures to run workloads
• Data types (type hints vs. registration API) — Provide the data types as parameters when creating a stored procedure to return as Python hints/specify them as part of the registration
• Create Directed Acyclic Graphs (tasks) executing stored procedures — Python API
• Bring Python modules (packaged code) to be used with UDFs — Stored procedures to enable reuse of code

Summary: This section expands into Python-driven stored procedures, showing how they help execute complex workloads inside Snowflake. By writing functions, registering procedures, and providing data type definitions, you ensure your logic integrates seamlessly into Snowpark applications. This supports scalable and reusable workflows tailored to your organization's needs.

Key content also focuses on dependency management, execution rights, and security considerations. You will become skilled in deploying stored procedures with proper caller versus owner permissions, aligning with Snowflake's security posture. The ability to integrate stored procedures with Directed Acyclic Graphs further extends process automation and orchestration capabilities.

Domain 3: Snowpark for Data Transformations (35% of the exam)

Apply operations for filtering and transforming data

• Use scalar functions and operators
• Sort and limit results
• Input/output (parameters)
• Snowpark DataFrames
• Columns
• Data type casting
• Rows and data extraction from a Rows object

Summary: This section introduces key operations for filtering, transforming, and refining data directly with Snowpark DataFrames. You will use functions, sorting, parameterized operations, and data casting as part of creating flexible transformations. This knowledge ensures accuracy and expressiveness in data preparation steps.

Additionally, the section emphasizes data extraction using row objects and applying custom logic in transformations. With these techniques, you can shape datasets for advanced downstream usage, from analytics to machine learning. Mastery of these DataFrame techniques builds the foundation for more complex aggregate and set-based operations later.

Clean and enrich data using Snowpark for Python

• Perform joins
• Handle missing values
• Sample data

Summary: This section focuses on working with real-world datasets that require cleaning and enrichment. You will practice performing joins effectively, addressing missing values, and preparing representative samples for testing or analytics. These steps align with typical data engineering workflows and strengthen data reliability.

By managing enrichment tasks in Snowpark, you ensure preparation happens inside the Snowflake platform, avoiding bottlenecks in other tools. The emphasis is on simplifying ETL pipelines by pushing operations into Snowpark, minimizing friction, and increasing efficiency.

Perform aggregate and set-based operations on DataFrames

• Functions
• Window
• Grouping
• Table functions
• UDFs

Summary: This section moves into aggregation and set-based logic for powerful data summarization. You will apply grouping functions, window functions, and compute across sets for practical use cases like metrics, partitioning, and cumulative calculations.

The inclusion of table functions and UDFs gives even more flexibility in producing advanced aggregation. Learning how to handle these in Snowpark ensures you can translate traditional SQL logic into programmatic workflows while still enjoying the benefits of scalability.

Transform semi-structured data in DataFrames

• Traverse semi-structured data
• Explicitly cast values in semi-structured data
• Flatten an array of objects into rows
• Load semi-structured data into DataFrames

Summary: This section explores Snowpark's ability to work with semi-structured data, such as JSON and XML. You will gain experience flattening object arrays, traversing structures, and casting values for accurate integration with typed schemas. These techniques broaden Snowpark’s ability to handle diverse data sources.

By loading semi-structured data into DataFrames, you unlock the ability to join, filter, and analyze data that might otherwise require specialized tools. This capability highlights how Snowpark can unify structured and semi-structured workflows within Snowflake seamlessly.

Persist the results of Snowpark DataFrames

• Create views from DataFrames
• Save DataFrame results as Snowflake tables
• Save DataFrame results as files in a stage

Summary: This section focuses on persistence strategies for DataFrame results. You will learn to create views for reuse, persist outputs into Snowflake tables, and save results externally through staging files. Each option empowers you to choose the right persistence model depending on workload requirements.

By practicing persistence methods, you ensure your processed outputs become reliable sources of truth for other tools and teams. This enables efficient handoffs between different stages of the data lifecycle, from development to analytics and operations.

Perform DML operations using Snowpark DataFrames

• Delete data
• Update data
• Insert data
• Merge data

Summary: This section covers Data Manipulation Language (DML) operations directly through Snowpark DataFrames. You will be able to apply updates, inserts, deletes, and merge logic without switching contexts back to raw SQL.

Running DML in Snowpark encourages end-to-end workflows entirely inside a single environment. By learning these techniques, you increase consistency and streamline complex processes typically spread across multiple systems.

Domain 4: Snowpark Performance Optimization (20% of the exam)

Configure Snowpark-optimized warehouses

• Use cases for Snowpark-optimized virtual warehouses
• Modify Snowpark-optimized virtual warehouse properties
• Billing for Snowpark-optimized virtual warehouses
• When to scale up/down virtual warehouses

Summary: This section introduces Snowpark-optimized warehouses and their role in performance tuning. You will explore how to configure properties, understand billing implications, and apply usage strategies across workloads.

Knowing how to scale resources effectively aligns to cost optimization and responsiveness in processing. You will see how Snowpark-optimized warehouses provide balance between speed and efficiency, enabling maximum value for applications.

Enhance performance in Snowpark applications

• Materialize results (caching) — Caching DataFrames (using .cache_result()) and understanding why this is useful
• Materialize results (caching) — Create a temporary table
• Vectorization — Understanding the difference between vectorized and scalar UDFs
• Vectorization — Vectorized UDFs for batching
• Vectorization — Snowpark DataFrames versus pandas on Snowflake
• Synchronous versus asynchronous calls — Block parameter

Summary: This section explains performance-enhancing techniques that elevate Snowpark applications. You will learn how caching with DataFrames or temporary tables reduces redundant computations, thereby saving both time and cost.

You will also explore advanced options like vectorized UDFs and asynchronous job handling. These topics highlight fine-grained control over parallelism and batching that make Snowpark both fast and scalable across data-intensive scenarios.

Troubleshoot common errors in Snowpark

• Event tables
• Snowpark Python local testing framework
• Writing tests (pyTest)
• Query history (SQL equivalency to help identify bottlenecks)

Summary: This section covers practices for recognizing and troubleshooting application issues. By studying event tables and analyzing query history with SQL equivalency, you gain the ability to detect bottlenecks effectively.

You will also learn about testing, both through Snowpark’s local framework and pytest. Mastery here ensures issues can be addressed proactively at the development stage, reinforcing secure and efficient workloads.