dbt Analytics Engineering Certification Exam cheatsheet

Introduction

I'm preparing for the DBT exam and I used the  "dbt Analytics Engineering Certification Exam Study Guide" for searching for more information about the topics covered in the exam. I stored the links that I used in this blogpost, just helping you too. I hope you enjoy it.

I'll frequently update this blogpost. I just started with a couple of topics.

Topic 1: Developing dbt models

• Identifying and verifying any raw object dependencies
• Pro-tips for workflows
• Understanding core dbt materializations
• Materializations
• Conceptualizing modularity and how to incorporate DRY principles
• DRY
• Converting business logic into performant SQL queries
• Business logic in DBT
• Refactoring legacy SQL to dbt
• How dbt Can Help Solve 4 Common Data Engineering Pain Points
• Using commands such as run, test, docs and seed
• dbt Command reference
• Creating a logical flow of models and building clean DAGs
• Data modeling techniques for more modularity
• How we structure our dbt projects
• dbt DAG: Definition, Usage, and Examples
• Defining configurations in dbt_project.yml
• Model configurations
• Configs, properties, what are they?
• Configuring sources in dbt
• Source configurations
• Add sources to your DAG
• Source properties
• Using dbt Packages
• Packages
• Building dbt packages
• dbt packages

Topic 2: Debugging data modeling errors

• Understanding logged error messages
• Debug errors
• The definitive guide to debugging dbt
• Troubleshooting using compiled code
• Debug errors
• The definitive guide to debugging dbt
• Troubleshooting .yml compilation errors
• Debug errors
• The definitive guide to debugging dbt
• Distinguishing between a pure SQL and a dbt issue that presents itself as a SQL issue
• Developing and implementing a fix and testing it prior to merging

Topic 3: Monitoring data pipelines

• Understanding and testing the warehouse-level implications of a model run failing at different points in the DAG
• Understanding DAGs in dbt: Erring Models and Failing Tests
• DAG
• On DAGs, Hierarchies, and IDEs
• Understanding the general landscape of tooling

Topic 4: Implementing dbt tests

• Using generic, singular and custom tests on a wide variety of models and sources
• Add data tests to your DAG
• Writing custom generic data tests
• Understanding assumptions specific to the datasets being generated in models and to the raw data in the warehouse
• Best practices for workflows
• Add sources to your DAG
• Implementing various testing steps in the workflow.
• Add data tests to your DAG
• Data testing
• Data transformation process: 6 steps in an ELT workflow
• Ensuring data is being piped into the warehouse and validating accuracy against baselines

Topic 5: Deploying dbt jobs

• Understanding the differences between deployment and development environments
• Configuring development and deployment environments
• Configuring the appropriate tasks, settings and triggers for the job
• Understanding how a dbt job utilizes an environment in order to build database objects and artifacts
• Using dbt commands to execute specific models

Topic 6: Creating and Maintaining dbt documentation

• Updating dbt docs
• Implementing source, table, and column descriptions in .yml files
• Using dbt commands to generate a documentation site
• Using macros to show model and data lineage on the DAG

Topic 7: Promoting code through version control

• Understanding concepts and working with Git branches and functionalities
• Creating clean commits and pull requests
• Merging code to the main branch

Topic 8: Establishing environments in data warehouse for dbt

• Understanding environment’s connections
• Understanding the differences between production data, development data, and raw data