2. dbt Analytics Engineering Certification

The dbt Analytics Engineering Certification evaluates your ability to build, test, and maintain models for data accessibility while using dbt to apply engineering principles to analytics infrastructure.

Exam Domains

The exam covers the following domains:

1. Developing dbt Models

   • Identifying and verifying raw object dependencies

   • Understanding core dbt materializations (table, view, incremental, ephemeral)

   • Conceptualizing modularity and DRY principles

   • Converting business logic into performant SQL queries

   • Using commands: run, test, docs, seed

   • Creating logical model flows and clean DAGs

   • Defining configurations in dbt_project.yml

   • Configuring sources in dbt

   • Using dbt Packages

   • Git functionality within the development lifecycle

   • Creating Python models

   • Providing access with the grants configuration

2. Understanding dbt Model Governance

   • Adding contracts to models to ensure shape consistency

   • Creating model versions and deprecating old ones

   • Configuring model access levels

3. Debugging Data Modeling Errors

   • Understanding logged error messages

   • Troubleshooting using compiled code

   • Troubleshooting YAML compilation errors

   • Distinguishing pure SQL vs dbt-related issues

   • Developing, implementing, and testing fixes before merging

4. Managing Data Pipelines

   • Troubleshooting and managing DAG failure points

   • Using dbt clone

   • Troubleshooting errors from integrated tools

5. Implementing dbt Tests

   • Using generic, singular, custom, and custom generic tests

   • Testing assumptions for models and sources

   • Implementing testing steps in the workflow

6. Creating and Maintaining dbt Documentation

   • Updating dbt docs

   • Implementing source, table, and column descriptions in YAML files

   • Using macros to show model and data lineage on the DAG

7. Implementing and Maintaining External Dependencies

   • Implementing dbt exposures

   • Implementing source freshness

8. Leveraging the dbt State

   • Understanding state

   • Using dbt retry

   • Combining state and result selectors

Recommended Learning Path

1. Foundation

   • Complete the dbt Fundamentals course on dbt Learn

   • Set up a local dbt project with sample data

   • Understand project structure and dbt_project.yml

2. Model Development

   • Practice building staging, intermediate, and mart models

   • Implement different materializations

   • Learn Jinja and macros basics

3. Testing and Documentation

   • Implement generic and singular tests

   • Write comprehensive model documentation

   • Generate and review dbt docs

4. Advanced Topics

   • Study model governance features (contracts, versions, access)

   • Practice with dbt state and selectors

   • Work with packages and exposures

5. Exam Preparation

   • Review the official study guide

   • Practice debugging scenarios

   • Take practice assessments

Official Study Guide (PDF)

External Resources

• Official Exam Registration

• dbt Documentation

• dbt Learn