SnowPro Advanced: Data Engineer Preparation Details
The SnowPro Advanced: Data Engineer (DEA-C02) exam validates advanced skills in building, optimizing, and governing data pipelines on Snowflake. This guide walks through every domain, task, and objective in the official exam guide, pairing each with Snowflake’s own documentation. You can also explore more Snowflake certification study guides on the Snowflake Certifications category to keep building your skills.
SnowPro Advanced: Data Engineer Materials
| Coursera | Snowflake Data Engineering Professional Certificate |
| Udemy | SnowPro Advanced Data Engineer Practice Sets |
Domain 1.0: Data Movement
1.1 Given a data set, load data into Snowflake.
Outline considerations for data loading
Define data loading features and potential impacts
General file loading considerations
1.2 Ingest data of various formats through the mechanics of Snowflake.
Required file formats
Schema detection using INFER_SCHEMA for table design and data analysis
Detecting column definitions in staged files
Ingestion of structured, semi-structured, and unstructured data
Introduction to semi-structured data
Introduction to unstructured data
Implementation of stages and file formats
Understanding end-to-end encryption in Snowflake
Extract metadata from staged files
Querying metadata for staged files
1.3 Troubleshoot data ingestion.
Identify causes of ingestion errors
Determine resolutions for ingestion errors
1.4 Design, build, and troubleshoot continuous data pipelines.
Stages
Tasks
Streams
Dynamic tables
Decision guide for dynamic tables
Materialized views
Working with materialized views
Snowpipe (for example, Auto Ingest compared to the REST API)
Automating Snowpipe for Amazon S3
Snowpipe Streaming
Overview of Snowpipe Streaming
Overview of the Kafka connector
Snowpipe Streaming compared to the Kafka connector
Overview of Snowpipe Streaming
Overview of the Kafka connector
Create User-Defined Functions (UDFs)
Overview of user-defined functions
Design and use the Snowflake SQL API
Submitting requests to execute SQL statements
Openflow
Use Notebooks to run pipelines of stored procedures for data ingestion tasks
Snowflake Notebooks in Workspaces
Use Snowflake scripting to develop and automate pipelines
Snowflake Scripting Developer Guide
1.5 Install, configure, and use connectors for Snowflake integration.
Kafka connectors
Overview of the Kafka connector
Installing and configuring the Kafka connector
Spark connectors
Overview of the Spark connector
Installing and configuring the Spark connector
Python connectors
Snowflake Connector for Python
Installing the Python connector
Native connectors
About the Snowflake Connector for ServiceNow
1.6 Design and build data sharing and data consumption solutions.
Evaluate the use of a data share or a clone
Introduction to Secure Data Sharing
Implement a data share
Getting started as a data provider
Introduction to Secure Data Sharing
Manage auto-fulfillment
Create and manage views
Implement row-level filtering
Understanding row access policies
Share data using the Snowflake Marketplace
Share data using a listing
Use Streamlit to build data applications and interfaces for data consumption
Create interactive dashboards for data exploration and sharing
Build self-service data access applications
1.7 Manage different types of tables and data operations.
Manage external tables
Introduction to external tables
Manage Iceberg tables
Manage hybrid tables
Perform general table management
Databases, tables, and views overview
Use Horizon Catalog to federate data from external catalogs
Query Apache Iceberg tables with an external engine through Snowflake Horizon Catalog
Manage schema evolution
Enable automatic table schema evolution
Unload data
Domain 2.0: Performance Optimization
2.1 Troubleshoot underperforming queries.
Identify underperforming queries
Analyzing queries using Query Profile
Outline telemetry around the operation
Analyzing queries using Query Profile
Identify the root cause
Analyze Workload Performance with CoCo
Increase efficiency
2.2 Given a scenario, configure a solution for optimal performance.
Scale out compared to scale up
Virtual warehouse properties (for example, size, multi-cluster)
Snowpark-optimized virtual warehouses
Query complexity
Analyzing queries using Query Profile
Micro-partitions and the impact of clustering
Micro-partitions and data clustering
Clustering keys and clustered tables
Materialized views
Working with materialized views
Search optimization service
Query acceleration service
Using the Query Acceleration Service (QAS)
Snowpark-optimized warehouses
Caching features
Use the ACCOUNT_USAGE schema
Use warehouse metrics (such as warehouse queues) and configurations:
Working with resource monitors
Resource monitors
Working with resource monitors
Warehouse constraints on credit consumption
Balance optimization with credit consumption considerations
Optimize storage configurations and costs
2.3 Monitor continuous data pipelines.
Snowflake objects
Overview of Snowpipe Streaming
Notifications
Configuring tasks to send error notifications
Data quality and data metric function monitoring
Data quality and data metric functions
Domain 3.0: Storage & Data Protection
3.1 Implement and manage data recovery features in Snowflake.
Time Travel
Understanding & using Time Travel
Impact of streams
Understanding & using Time Travel
Fail-safe
Understanding & using Fail-safe
Cross-region and cross-cloud replication
Share data securely across regions and cloud platforms
Introduction to replication for business continuity
3.2 Use system functions to analyze micro-partitions.
Clustering depth
Cluster keys
Clustering keys and clustered tables
Automatic Clustering features and optimizations
3.3 Use Time Travel and cloning to create new development environments.
Clone objects
Permission inheritance
Validate changes before promoting
Understanding & using Time Travel
Rollback changes
Understanding & using Time Travel
Domain 4.0: Data Governance
4.1 Monitor data.
Apply object tagging and classifications
Getting started with data classification
Use data classification to monitor data
Getting started with data classification
Manage data lineage and object dependencies
Using data lineage in Snowsight
Monitor data quality
Data quality and data metric functions
4.2 Establish and maintain data protection.
Use Horizon Catalog to share and federate data outside of Snowflake
Implement column-level security
Understanding Dynamic Data Masking
Understanding External Tokenization
Understanding projection policies
Use in conjunction with Dynamic Data Masking
Understanding Dynamic Data Masking
Use in conjunction with external tokenization
Understanding External Tokenization
Use projection policies
Understanding projection policies
Use data masking with Role-Based Access Control (RBAC) to secure sensitive data
Understanding Dynamic Data Masking
Explain the options available to support row-level security using Snowflake row access policies
Understanding row access policies
Use aggregation policies
Implementing entity-level privacy with aggregation policies
Use DDL to manage Dynamic Data Masking and row access policies
Use best practices to create and apply data masking policies
Understanding Dynamic Data Masking
Use Snowflake Data Clean Rooms to share data
About Snowflake Data Clean Rooms
Clean room UI
Overview of Snowflake Data Clean Rooms
Snowflake developer APIs
Data Clean Rooms Developer Guide
Snowflake Data Clean Rooms developer’s guide
Domain 5.0: Data Transformation
5.1 Define User-Defined Functions (UDFs) and outline how to use them.
Snowpark UDFs (for example, Java, Python, Scala)
Creating user-defined functions (UDFs) for DataFrames in Python
Secure UDFs
Protecting sensitive information with Secure UDFs and stored procedures
SQL UDFs
JavaScript UDFs
User-Defined Table Functions (UDTFs)
Overview of user-defined functions
User-Defined Aggregate Functions (UDAFs)
5.2 Define and create external functions.
Secure external functions
Protecting sensitive information with Secure UDFs and stored procedures
Work with external functions
Introduction to external functions
5.3 Design, build, and leverage stored procedures.
Snowpark stored procedures
Writing stored procedures in Snowpark (Python)
SQL Scripting stored procedures
Snowflake Scripting Developer Guide
JavaScript stored procedures
Transaction management
5.4 Handle and transform semi-structured data.
Traverse and transform semi-structured data to structured data
Transform structured data to semi-structured data
5.5 Handle and process unstructured data.
Use unstructured data
Introduction to unstructured data
URL types
Types of URLs available to access files
Use directory tables
Use the Rest API
Use semantic views
Use Snowflake Cortex features to:
Automate data categorization
Extract multimedia data
Cortex AI Functions: Documents
Perform semantic data analysis
Run text analytics in data pipelines
Run multimodal AI workflows within SQL queries
Cortex AI Functions: Documents
Use Cortex LLM for cost management
Cost considerations for Cortex AI Functions
Managing Cortex AI Function costs with Account Usage
5.6 Implement and manage development workflows and code management.
Snowsight Workspaces and development environments
Snowflake Notebooks
Snowflake Notebooks in Workspaces
Git integration and version control
Using a Git repository in Snowflake
Setting up Snowflake to use Git
Snowflake dbt Projects management
Code deployment pipelines
Testing and validation frameworks
Environment management strategies
5.7 Use Snowpark for data transformations.
Understand Snowpark architecture
Query and filter data using the Snowpark library
Working with DataFrames in Snowpark Python
Perform data transformations using Snowpark (for example, aggregations)
Working with DataFrames in Snowpark Python
Manipulate Snowpark DataFrames
Working with DataFrames in Snowpark Python
Wrapping Up SnowPro Advanced: Data Engineer
That covers every domain of the SnowPro Advanced: Data Engineer (DEA-C02) exam guide, from data movement and performance tuning through storage protection, governance, and transformation. Work through the linked documentation for each objective, then test yourself against the official sample questions. You can also explore more Snowflake certification study guides on the Snowflake Certifications category to keep building your skills. Have a question or tip? Leave a comment below.
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