Free Practice Questions for Snowflake DEA-C02 Certification
- Exam guide version:
- March 6, 2026
- Guide checked for updates:
- 9 Oct 2026
- Question bank created:
- 16 Sep 2026
- Question bank last updated:
- 7 Oct 2026
Study with 388 exam-style practice questions designed to help you prepare for the Snowflake SnowPro Advanced: Data Engineer (DEA-C02). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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LessonsNew
22 documentation-grounded lessons, one per exam-guide subdomain — every claim cited to the official docs.Based on the official docs as of 4 Oct 2026
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Exam Details
Key information about Snowflake SnowPro Advanced: Data Engineer (DEA-C02)
- Multiple choice
associate (intermediate)
Through Snowflake Continuing Education (CE) program (eligible ILT courses or higher-level certification)
August 22, 2025
Active SnowPro Core Certified credential
Data Engineers and Software Engineers with 2+ years of data engineering experience, including practical Snowflake usage, RESTful APIs, SQL, semi-structured data, and cloud-native concepts
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
1: Data Movement
1.1: Given a data set, load data into Snowflake.
- Outline considerations for data loading - Define data loading features and potential impacts
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 - Ingestion of structured, semi-structured, and unstructured data - Implementation of stages and file formats - Manage storage integrations configurations - Manage encryption (pre-scoped URLs, server-side, or client-side) - Manage compression and parsing strategies - Extract metadata from 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 - Materialized views - Snowpipe (for example, Auto Ingest compared to the REST API) - Snowpipe Streaming - Snowpipe Streaming compared to the Kafka connector - Create User-Defined Functions (UDFs) - Design and use the Snowflake SQL API - Openflow - Use Notebooks to run pipelines of stored procedures for data ingestion tasks - Use Snowflake scripting to develop and automate pipelines
1.5: Install, configure, and use connectors for Snowflake integration.
- Kafka connectors - Spark connectors - Python connectors - Native connectors
1.6: Design and build data sharing and data consumption solutions.
- Evaluate the use of a data share or a clone - Implement a data share - Manage auto-fulfillment - Create and manage views - Implement row-level filtering - 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 - Manage Iceberg tables - Manage hybrid tables - Perform general table management - Use Horizon Catalog to federate data from external catalogs - Manage schema evolution - Unload data
2: Performance Optimization
2.1: Troubleshoot underperforming queries.
- Identify underperforming queries - Outline telemetry around the operation - Identify the root cause - 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 - Micro-partitions and the impact of clustering - Materialized views - Search optimization service - Query acceleration service - Snowpark-optimized warehouses - Caching features - Use the ACCOUNT_USAGE schema - Use warehouse metrics (such as warehouse queues) and configurations: - 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 - Tasks - Snowsight task history dashboards - Streams - Snowpipe Streaming - Alerts - Dynamic Tables - Notifications - Data quality and data metric function monitoring
3: Storage & Data Protection
3.1: Implement and manage data recovery features in Snowflake.
- Time Travel - Impact of streams - Fail-safe - Cross-region and cross-cloud replication
3.2: Use system functions to analyze micro-partitions.
- Clustering depth - Cluster keys - 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 - Rollback changes
4: Data Governance
4.1: Monitor data.
- Apply object tagging and classifications - Use data classification to monitor data - Manage data lineage and object dependencies - Monitor data quality
4.2: Establish and maintain data protection.
- Use Horizon Catalog to share and federate data outside of Snowflake - Implement column-level security - Use in conjunction with Dynamic Data Masking - Use in conjunction with external tokenization - Use projection policies - Use data masking with Role-Based Access Control (RBAC) to secure sensitive data - Explain the options available to support row-level security using Snowflake row access policies - Use aggregation policies - Use DDL to manage Dynamic Data Masking and row access policies - Use best practices to create and apply data masking policies - Use Snowflake Data Clean Rooms to share data - Clean room UI - Snowflake developer APIs
5: Data Transformation
5.1: Define User-Defined Functions (UDFs) and outline how to use them.
- Snowpark UDFs (for example, Java, Python, Scala) - Secure UDFs - SQL UDFs - JavaScript UDFs - User-Defined Table Functions (UDTFs) - User-Defined Aggregate Functions (UDAFs)
5.2: Define and create external functions.
- Secure external functions - Work with external functions
5.3: Design, build, and leverage stored procedures.
- Snowpark stored procedures - SQL Scripting stored procedures - 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 - URL types - Use directory tables - Use the Rest API - Use semantic views - Use Snowflake Cortex features to: - Automate data categorization - Extract multimedia data - Perform semantic data analysis - Run text analytics in data pipelines - Run multimodal AI workflows within SQL queries - Use Cortex LLM for cost management
5.6: Implement and manage development workflows and code management.
- Snowsight Workspaces and development environments - Snowflake Notebooks - Git integration and version control - Snowflake dbt Projects management - Code deployment pipelines - Testing and validation frameworks - Environment management strategies
5.7: Use Snowpark for data trans- formations.
- Understand Snowpark architecture - Query and filter data using the Snowpark library - Perform data transformations using Snowpark (for example, aggregations) - Manipulate Snowpark DataFrames
Techniques & products