Snowflake SnowPro Advanced Data Engineer Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Data Movement (27% of the exam)

Section: Given a data set, load data into Snowflake.

• Outline considerations for data loading
• Define data loading features and potential impact

Section summary: This section focuses on the critical process of efficiently loading data into Snowflake. You will deepen your understanding of methods for ingesting diverse datasets while considering cost, performance, and architectural best practices. Key areas include choosing the right loading strategy depending on data size and frequency, as well as managing metadata and monitoring job execution.

You will also explore the potential impact of different loading features, ensuring that you can balance operational efficiency with enterprise requirements. This section ensures you recognize not just how to load information but also how to design ingestion processes that maximize elasticity and minimize bottlenecks.

Section: Ingest data of various formats through the mechanics of Snowflake.

• Required data formats
• Outline stages

Section summary: This section emphasizes ingesting structured and semi-structured data formats into Snowflake using native capabilities. Topics include handling CSV, JSON, parquet, and AVRO files, along with recognition of how Snowflake manages these data types seamlessly.

You will also focus on stages within Snowflake such as user, table, and internal stages, along with external cloud storage staging. Expect to strengthen your understanding of how staging layers simplify the ingestion process while optimizing for scale.

Section: Troubleshoot data ingestion.

• Identify causes of ingestion errors
• Determine resolutions for ingestion errors

Section summary: This section explores techniques for diagnosing issues that arise during the ingest process. You’ll practice looking at system logs, error messages, and identifying common root causes such as mismatched file formats or permissions.

The second focus is on resolutions and applying best practices for keeping data ingestion resilient. You’ll learn how to streamline pipelines by addressing schema mismatches, retry strategies, and proper use of copy options for consistent data flow.

Section: Design, build and troubleshoot continuous data pipelines.

• Stages
• Tasks
• Streams
• Snowpipe (Auto ingest compared to Rest API)

Section summary: This section highlights the design and orchestration of continuous data pipelines. You’ll work with the tools that Snowflake provides to support continuous data capture, including streams and tasks, which allow for incremental processing.

It also emphasizes Snowpipe as a mechanism for near-real-time ingestion. You’ll learn to compare auto-ingest configurations with REST API approaches, ensuring the correct design choice for balancing latency and complexity in a pipeline.

Section: Analyze and differentiate types of data pipelines.

• Create User-Defined Functions (UDFs) and stored procedures including Snowpark
• Design and use the Snowflake SQL API

Section summary: This section introduces extended capabilities for creating modular, reusable data pipelines. UDFs, stored procedures, and Snowpark enable engineers to enrich or transform data programmatically while integrating with broader data processes.

You’ll also examine how to incorporate the SQL API into robust solutions, blending API-driven workflows with Snowflake-native capabilities. The emphasis is on understanding design tradeoffs between declarative SQL and more programmatic approaches.

Section: Install, configure, and use connectors to connect to Snowflake.

Section summary: This section ensures you can integrate Snowflake with other tools and platforms using various connectors such as JDBC, ODBC, and streaming connectors. These integration points are vital for maintaining compatibility and supporting enterprise-wide architectures.

In practice, connectors serve as bridges to external platforms for analysis, ingest, or application connectivity. By mastering their configuration, you ensure seamless end-to-end pipeline experiences.

Section: Design and build data sharing solutions.

• Implement a data share
• Create a secure view
• Implement row level filtering

Section summary: This section deepens your understanding of secure data sharing. You’ll learn how to set up shares and securely deliver governed datasets to partners or across business units without duplicating data.

Practical areas include creating secure views and applying row-level filters to meet fine-grained access requirements. The focus is on enabling collaboration while maintaining complete control over data access and visibility.

Section: Outline when to use external tables and define how they work.

• Partitioning external tables
• Materialized views
• Partitioned data unloading

Section summary: This section focuses on extending Snowflake functionality to external tables, which allow the query of data residing outside of native storage. You’ll learn when to appropriately configure external tables to address governance and performance needs.

Complementing this is a review of related features such as materialized views for accelerating performance and partitioned unloading work to handle large output sets efficiently. The emphasis is on seeing external data as a first-class component of the data architecture.

Domain 2: Performance Optimization (22% of the exam)

Section: Troubleshoot underperforming queries.

• Identify underperforming queries
• Outline telemetry around the operation
• Increase efficiency
• Identify the root cause

Section summary: This section builds the skills needed to identify and resolve performance issues. You’ll analyze queries, interpret execution plans, and identify patterns that indicate inefficiency.

Equally important is learning to apply telemetry and monitoring data for deeper insight. The result is developing proactive approaches to not just recognize underperformance but also resolve it quickly and sustainably.

Section: Given a scenario, configure a solution for the best performance.

• Scale out compared to scale up
• Virtual warehouse properties (size, multi-cluster)
• Query complexity
• Micro-partitions and clustering
• Materialized views
• Search optimization service
• Query acceleration service

Section summary: This section refines your decision-making skills around performance-related configuration. You’ll compare strategies between scaling out and scaling up, configuring warehouses to match workloads, and applying clustering appropriately.

The goal is to understand how multiple performance services such as search optimization or query acceleration can elevate responsiveness. Overall, you’ll learn to align each feature with the workload and enterprise goals.

Section: Outline and use caching features.

Section summary: This section helps you fully leverage Snowflake’s caching layers to accelerate performance. You’ll learn how different cache layers work, from query result caching to local storage, and when each is applied.

Practical use cases for caching include improving query experience while reducing compute cost. This ensures not only efficiency but also a superior user experience.

Section: Monitor continuous data pipelines.

• Snowpipe
• Tasks
• Streams

Section summary: This section focuses on monitoring and ensuring the health of continuous pipelines. You will learn how monitoring with Snowpipe or tasks offers clear visibility into backlogs, throughput, and latency.

With streams actively involved in change data capture, this monitoring allows you to validate end-to-end functionality of near-real-time architectures. The emphasis is on reliability and predictability in pipeline performance.

Domain 3: Storage and Data Protection (12% of the exam)

Section: Implement data recovery features in Snowflake.

• Time Travel
• Fail-safe

Section summary: This section highlights the built-in protection mechanisms that Snowflake offers. Time Travel enables reverting to historical states of data, while Fail-safe provides an extra safeguard beyond user-controlled recovery windows.

Together, these capabilities ensure resilience and support compliance, making it possible to respond quickly to potential data loss or accidental deletion scenarios.

Section: Outline the impact of streams on Time Travel.

Section summary: This section explores how streams interact with Snowflake’s Time Travel features. Streams allow for continuous change tracking, and their relationship with Time Travel ensures that even historical modifications can be accurately tracked.

By examining this interaction, you’ll understand how to preserve consistency across both immediate ingestion and retrospective recovery requirements.

Section: Use system functions to analyze micro-partitions.

• Clustering depth
• Cluster keys

Section summary: This section provides detailed insights into how Snowflake stores and organizes data. You’ll learn system functions that reveal clustering depth and how cluster keys support performance at scale.

This analysis enables deeper optimization, particularly when scaling to very large datasets, ensuring efficient pruning and query responsiveness.

Section: Use Time Travel and cloning to create new development environments.

• Clone objects
• Validate changes before promoting
• Rollback changes

Section summary: This section emphasizes the power of cloning and Time Travel for agile data development. Engineers can instantly and cost-effectively replicate production environments for testing or development.

The ability to validate and rollback changes makes experiment-driven workflows extremely efficient. This ensures confidence and quality while promoting changes to higher environments.

Domain 4: Security (12% of the exam)

Section: Outline Snowflake security principles.

• Authentication methods (SSO, key pair, password, MFA)
• Role Based Access Control (RBAC)
• Column level security and data masking with RBAC

Section summary: This section provides a foundation in Snowflake’s layered security model. You’ll review authentication methods and how RBAC applies permissions cleanly and efficiently across an organization.

Column-level security and dynamic masking give even more precision, enabling businesses to share data safely while maintaining compliance.

Section: Outline the system defined roles and when they should be applied.

• Purpose of system defined roles
• Best practices for applying roles
• Differences between SECURITYADMIN, USERADMIN, and SYSADMIN

Section summary: This section explains the purpose of built-in system roles within Snowflake. By understanding the responsibilities of SECURITYADMIN, USERADMIN, and SYSADMIN, you’ll be able to assign authority safely and productively.

Best practices ensure privileges are distributed responsibly, promoting separation of duties and protecting core operations without introducing risk.

Section: Manage data governance.

• Options for column level security including Dynamic Data Masking and external tokenization
• Options for row level security using row access policies
• Use DDL required to manage these controls
• Apply masking policies to data
• Understand object tagging

Section summary: This section shifts the focus to identifying governance patterns for sensitive or regulated data. You’ll evaluate methods such as row and column-level security along with tagging for downstream governance integration.

By mastering DDL for access controls, you gain hands-on insight into enforcing governance strategies directly in Snowflake. The result is trusted and secure data governance aligned with organizational requirements.

Domain 5: Data Transformation (27% of the exam)

Section: Define User-Defined Functions (UDFs) and outline how to use them.

• Snowpark UDFs (Java, Python, Scala)
• Secure UDFs
• SQL UDFs
• JavaScript UDFs
• User-Defined Table Functions (UDTFs)

Section summary: This section dives into extensibility provided by UDFs in Snowflake. You’ll compare different formats such as SQL-based, JavaScript, and Snowpark UDFs to enhance transformation logic.

The primary focus is on how these UDFs can encapsulate business rules or transformation steps that traditional SQL cannot handle easily.

Section: Define and create external functions.

• Secure external functions
• Integration requirements

Section summary: This section introduces the ability to connect and query external services through external functions. You’ll understand security and integration considerations while designing real-time callouts to APIs during queries.

This delivers a powerful approach to blending Snowflake workloads with wider API-driven architectures.

Section: Design, build, and leverage stored procedures.

• Snowpark stored procedures (Java, Python, Scala)
• SQL Scripting stored procedures
• JavaScript stored procedures
• Transaction management

Section summary: This section offers a deeper focus on creating reusable stored procedures for advanced processing. From Snowpark powered designs to JavaScript and SQL scripting, stored procedures extend engineering capabilities within data workflows.

Transaction management is included here, equipping engineers for consistency across multi-step data operations.

Section: Handle and transform semi-structured data.

• Traverse and transform semi-structured data into structured
• Transform structured data into semi-structured
• Understand how to work with unstructured data

Section summary: This section highlights Snowflake’s ability to work seamlessly across structured and semi-structured data. You’ll practice transforming JSON, XML, or Parquet data, optimizing queries for readability and high performance.

Additionally, you’ll expand your proficiency in bridging structured and unstructured approaches, enabling flexibility in the way data-driven applications are supported.

Section: Use Snowpark for data transformation.

• Understand Snowpark architecture
• Query and filter data using the Snowpark library
• Perform data transformations with Snowpark (aggregations)
• Manipulate Snowpark DataFrames

Section summary: This section emphasizes the role of Snowpark in delivering more programmatic control over data operations. By combining familiar programming languages with DataFrame constructs, it provides developers with added flexibility.

From querying to complex transformations and aggregations, Snowpark adds powerful capabilities for building pipelines or machine learning features within Snowflake.