dbt Analytics Engineering Certification
Study notes for every exam objective
One note per objective in the published exam guide: what it covers, and the facts the exam actually tests. 31 objectives are written up. Coverage last checked against the guide on 2026-08-05.
Developing and optimizing dbt models
45.16% of the exam · 14 notes- Tracing and confirming a model's upstream raw sources
- How dbt's core materialization types differ
- Keeping models modular and avoiding repeated logic
- What each core dbt command actually does
- Shaping a model's dependency graph so it stays readable
- Setting project-wide configuration in dbt_project.yml
- Pulling in and using a dbt package
- Writing a model in Python instead of SQL
- Granting model access through the grants config
- Configuring a snapshot to track slowly changing data
- Picking the right incremental strategy for a dataset
- Dry-running a model to check its logic and schema before a real build
- Sampling a model run before a full build
- What microbatch and other advanced materializations are for