SNOWFLAKESnowflake SnowPro Advanced — Data Engineer
The SnowPro Advanced: Data Engineer certification validates your expertise in designing and implementing complex data engineering solutions on the Snowflake AI Data Cloud. It is for experienced data engineers who build and manage robust, scalable data pipelines and transformations. Earning it demonstrates your ability to apply advanced Snowflake features to solve real-world data challenges and stand out in the data community.
290 practice questions · Updated 2026-07-30
5Domains
13Objectives
104Concepts
290Questions
DEA-C02 Curriculum
Every domain, objective, and concept the DEA-C02 exam measures.
- COPY INTO command syntax
- File format options for COPY INTO
- Copy options for error handling
- Loading from named stages
- Loading from external locations
- Transformation during load
- Validation and dry-run loads
- Unloading data with COPY INTO
- Unload file format and options
- Snowpipe overview
- Creating and managing pipes
- Snowpipe file notification and auto-ingest
- Snowpipe error handling and monitoring
- Snowpipe cost and performance considerations
- Stage types and usage
- Creating and managing stages
- File format objects
- File format options
- Stage and file format integration
- Connectors overview
- Configuring connectors
- Stage security and access control
- Troubleshooting stage and file format issues
- Streaming ingestion overview
- Snowpipe for streaming
- Snowpipe Streaming API
- Kafka and Snowflake connector
- Kinesis and Snowflake connector
- Streaming ingestion best practices
- SQL transformation patterns
- Snowpark DataFrame API
- Snowpark stored procedures
- User-defined functions (UDFs)
- Streaming transformations
- Performance optimization for transformations
- Streams overview
- Creating and using streams
- Stream consumption and offset management
- Tasks overview
- Creating and scheduling tasks
- Task DAGs and dependencies
- Integrating streams and tasks
- Stream and task lifecycle management
- Stream behavior with DML and DDL
- Performance and cost considerations
- Parse JSON data
- Parse XML data
- Parse Avro data
- Parse Parquet data
- Parse ORC data
- Flatten nested structures
- Handle missing or null values
- Transform semi-structured data
- Clustering Key Fundamentals
- Choosing Clustering Keys
- Clustering Depth and Maintenance
- Clustering Costs and Trade-offs
- Search Optimization Service Overview
- Enabling and Managing Search Optimization
- Search Optimization vs. Clustering
- Warehouse Sizing Fundamentals
- Multi-Cluster Warehouses
- Warehouse Sizing Best Practices
- Query Pruning Concepts
- Micro-Partitions and Clustering
- Pruning Optimization Techniques
- Monitoring and Tuning
- Identify pipeline bottlenecks
- Optimize pipeline performance
- Monitor pipeline health
- Tune pipeline configurations
- Time Travel retention period
- Setting Time Travel retention
- Querying historical data
- Cloning with Time Travel
- Restoring dropped objects
- Time Travel costs and storage
- Fail-safe overview
- Fail-safe data recovery
- Fail-safe storage costs
- Differences between Time Travel and Fail-safe
- Clone types
- Clone creation
- Clone behavior
- Clone dependencies
- Clone limitations
- Time travel retention
- Fail-safe retention
- Retention parameter settings
- Retention inheritance
- Retention cost implications
- Retention and cloning interaction
- Masking policy basics
- Creating and applying masking policies
- Masking policy management
- Row access policy basics
- Creating and applying row access policies
- Row access policy management
- Policy evaluation and performance
- Policy testing and debugging
- Pipeline access control fundamentals
- Role-based access control (RBAC) for pipelines
- Secure pipeline execution
- Monitoring and auditing pipeline access
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for DEA-C02, so none is invented.