DBT Analytics Engineering Certification Exam Questions

About this course

The Analytics Engineering Certification Exam evaluates your ability to:build, test, and maintain models to make data accessible to othersuse dbt to apply engineering principles to analytics infrastructureWhat's covered in the exam?Developing dbt models:Identifying and verifying any raw object dependenciesUnderstanding core dbt materializationsConceptualizing modularity and how to incorporate DRY principlesConverting business logic into performant SQL queriesUsing commands such as run, test, docs and seedCreating a logical flow of models and building clean DAGsDefining configurations in dbt_project.ymlConfiguring sources in dbtUsing dbt PackagesUtilizing git functionality within the development lifecycleCreating Python ModelsProviding access to users to models with the “grants” configUnderstanding dbt models governance:Adding contracts to models to ensure the shape of modelsCreating different versions of our models and deprecating the old onesConfiguring Model AccessDebugging data modeling errors:Understanding logged error messagesTroubleshooting using compiled codeTroubleshooting .yml compilation errorsDistinguishing between dbt core or data platform error responsesDeveloping and implementing a fix and testing it prior to mergingManaging data pipelines:Troubleshooting and managing failure points in the DAGUsing dbt cloneTroubleshooting errors from integrated toolsImplementing dbt tests:Using gene

Course objectives

• Evaluate the ability to build and maintain data models
• Ensure data is accessible to users
• Understand dbt governance and its best practices