The Snowflake DEA-C02 exam preparation guide is designed to provide candidates with necessary information about the SnowPro Advanced - Data Engineer exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Snowflake Certified SnowPro Advanced - Data Engineer Certification exam.
It is recommended for all the candidates to refer the DEA-C02 objectives and sample questions provided in this preparation guide. The Snowflake SnowPro Advanced - Data Engineer certification is mainly targeted to the candidates who want to build their career in Advance domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Snowflake SnowPro Advanced - Data Engineer exam.
Snowflake DEA-C02 Exam Summary:
| Exam Name | Snowflake SnowPro Advanced - Data Engineer |
| Exam Code | DEA-C02 |
| Exam Price | $375 USD |
| Duration | 115 minutes |
| Number of Questions | 65 |
| Passing Score | 750 + Scaled Scoring from 0 - 1000 |
| Recommended Training / Books |
Snowflake Data Engineering Training SnowPro Advanced: Data Engineer Study Guide |
| Schedule Exam | PEARSON VUE |
| Sample Questions | Snowflake DEA-C02 Sample Questions |
| Recommended Practice | Snowflake Certified SnowPro Advanced - Data Engineer Certification Practice Test |
Snowflake SnowPro Advanced - Data Engineer Syllabus:
| Section | Objectives |
|---|---|
Data Movement - 28% |
|
| Given a data set, load data into Snowflake. |
- Outline considerations for data loading - Define data loading features and potential impacts |
| 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
- Extract metadata from staged files |
| Troubleshoot data ingestion. |
- Identify causes of ingestion errors - Determine resolutions for ingestion errors |
| 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
- Create User-Defined Functions (UDFs) |
| Install, configure, and use connectors for Snowflake integration. |
- Kafka connectors - Spark connectors - Python connectors - Native connectors |
| Design and build data sharing and data consumption solutions. |
- Evaluate the use of a data share or a clone - Implement a data share
- Create and manage views
|
| 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 |
Performance Optimization - 19% |
|
| Troubleshoot underperforming queries. |
- Identify underperforming queries - Outline telemetry around the operation - Identify the root cause - Increase efficiency |
| Given a scenario, configure a solution for optimal performance. |
- Scale out compared to scale up - Virtual warehouse properties (for example, size, multi-cluster)
- Query complexity
- Balance optimization with credit consumption considerations |
| Monitor continuous data pipelines. |
- Snowflake objects
- Notifications |
Storage & Data Protection - 14% |
|
| Implement and manage data recovery features in Snowflake. |
- Time Travel
- Fail-safe |
| Use system functions to analyze micro-partitions. |
- Clustering depth - Cluster keys - Automatic Clustering features and optimizations |
| Use Time Travel and cloning to create new development environments. |
- Clone objects
- Validate changes before promoting |
Data Governance - 14% |
|
| Monitor data. |
- Apply object tagging and classifications - Use data classification to monitor data - Manage data lineage and object dependencies - Monitor data quality |
| Establish and maintain data protection. |
- Use Horizon Catalog to share and federate data outside of Snowflake - Implement column-level security
- Use data masking with Role-Based Access Control (RBAC) to secure sensitive data
- Use DDL to manage Dynamic Data Masking and row access policies
|
Data Transformation - 25% |
|
| 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) |
| Define and create external functions. |
- Secure external functions - Work with external functions |
| Design, build, and leverage stored procedures. |
- Snowpark stored procedures - SQL Scripting stored procedures - JavaScript stored procedures - Transaction management |
| Handle and transform semi-structured data. |
- Traverse and transform semi-structured data to structured data - Transform structured data to semi-structured data |
| Handle and process unstructured data. |
- Use unstructured data
- Use directory tables
- Use Cortex LLM for cost management |
| Implement and manage development workflows and code management. |
- Snowsight Workspaces and development environments
- Git integration and version control |
| Use Snowpark for data transformations. |
- Understand Snowpark architecture - Query and filter data using the Snowpark library - Perform data transformations using Snowpark (for example, aggregations) - Manipulate Snowpark DataFrames |
