The Analytics Engineering Certification Exam evaluates your ability to:

• build, test, and maintain models to make data accessible to others

• use dbt to apply engineering principles to analytics infrastructure

What's covered in the exam?

Developing dbt models:

• Identifying and verifying any raw object dependencies

• Understanding core dbt materializations

• Conceptualizing modularity and how to incorporate DRY principles

• Converting business logic into performant SQL queries

• Using commands such as run, test, docs and seed

• Creating a logical flow of models and building clean DAGs

• Defining configurations in dbt_project.yml

• Configuring sources in dbt

• Using dbt Packages

• Utilizing git functionality within the development lifecycle

• Creating Python Models

• Providing access to users to models with the “grants” config

Understanding dbt models governance:

• Adding contracts to models to ensure the shape of models

• Creating different versions of our models and deprecating the old ones

• Configuring Model Access

Debugging data modeling errors:

• Understanding logged error messages

• Troubleshooting using compiled code

• Troubleshooting .yml compilation errors

• Distinguishing between dbt core or data platform error responses

• Developing and implementing a fix and testing it prior to merging

Managing data pipelines:

• Troubleshooting and managing failure points in the DAG

• Using dbt clone

• Troubleshooting errors from integrated tools

Implementing dbt tests:

• Using generic, singular, custom, and custom generic tests on a wide variety of models and sources

• Testing assumptions for dbt models and sources

• Implementing various testing steps in the workflow

Creating and Maintaining dbt documentation:

• Updating dbt docs

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

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

Implementing and Maintaining External Dependencies:

• Implementing dbt exposures

• Implementing source freshness

Leveraging the dbt state:

• Understanding state

• Using dbt retry

• Combining state and result selectors