📝 Study asset for:
- The Complete dbt Bootcamp and Certification Course
- the 2026 dbt Analytics Engineering Certification Practice Tests course on Udemy
This guide shows exactly where in the bootcamp each certification topic is taught, so you can jump straight to the right lectures when you revise for the dbt Analytics Engineering Certification (v1.11).
| Detail | Value |
|---|---|
| Supported dbt version | 1.11 (renewed April 2026) |
| Questions | 65 |
| Duration | 2 hours |
| Passing score | 65% (42 / 65) |
| Format | Online, live-proctored |
| Topics | 7 (Documentation is no longer a standalone topic — it folds into Topic 1) |
The certification is organised into 7 topics. Below, each topic lists its published sub-topics and the course section(s) that cover them. Section numbers refer to the course curriculum.
How to use this guide: Work through the course in order first — it's designed as a learning path, not a topic list. Then use the tables below as a revision checklist: for any topic you feel shaky on, go back to the linked sections before taking a practice test.
The largest topic on the exam. It spans most of the practical course.
| Sub-topic | Where it's taught |
|---|---|
| Identifying and verifying raw object dependencies | §5 Seeds and Sources — Sources, Source Freshness Checks |
| Core materializations | §4 Materializations — Materializations Overview, Table type materialization, Incremental materialization, Ephemeral materialization |
| Modularity & DRY principles | §3 Models; §9 Jinja, Macros and Packages — Create Your Own Macros, Advanced Macros in Action |
Commands (build, run, test, docs, show, snapshot, seed) |
§15 Debugging YAML, SQL, models and General dbt Bugs — An Overview of Advanced dbt Commands, Taking Debugging Commands to Action (and used throughout the course) |
| Building clean DAGs / logical model flow | §10 Documentation — The Lineage Graph (Data Flow DAG) |
dbt_project.yml configurations |
§2 Building the first version of our Project — dbt Project Setup, Say Hello to our dbt Project Folder |
| Using dbt Packages | §9 Jinja, Macros and Packages — Installing Third-Party Packages |
| Creating Python models | §17 Python Models — Python Models Overview, Implementing a Simple Python Model, Advanced Python Models |
grants configuration |
§11 Analyses, Hooks and Exposures — Grants - Managing Permissions in dbt the Modern Way |
| Creating snapshots in YAML | §6 Snapshots — Snapshots Overview, Create a Snapshot |
| Selecting the optimal incremental strategy | §4 Materializations — Incremental Strategies (append / merge / delete+insert / insert_overwrite) |
--empty flag (dry-run validation) |
§15 — Speeding up Development with --empty and Sampling (--sample) |
--sample flag (sample-mode runs) |
§15 — Speeding up Development with --empty and Sampling (--sample) |
| Microbatch materialization | §19 dbt in Production - Microbatching Incremental Models — Microbatching, Excluding Models from Full Refresh |
Also helpful: §16 Tags and Selectors (Tags, Model Selection Deep Dive, YAML Selectors) for node-selection syntax used by many commands.
| Sub-topic | Where it's taught |
|---|---|
| Adding contracts to models | §8 Advanced Testing: Contracts and Custom Generic Tests — Data Contracts |
| Creating model versions and deprecating old ones | §20 dbt in Production - Model Lifecycle — Model Versioning, Deploying and Deprecating Versions, Disabling Models |
| Defining constraints in YAML | §8 — dbt Constraints: Test at Insert Time |
The v1.11 blueprint does not test model
accesslevels (public/protected/private) orgroups. Versions and constraints are the headline governance items.
| Sub-topic | Where it's taught |
|---|---|
| Understanding logged error messages | §7 Tests — Debugging dbt Tests; §14 Debugging with Logging — Logging to the dbt Log File, Logging to the Screen, Disabling Log Messages |
| Troubleshooting using compiled code | §15 — Taking Debugging Commands to Action |
Troubleshooting .yml compilation errors |
§15 Debugging YAML, SQL, models and General dbt Bugs |
| Developing a fix and testing it before merging | §22 Implementing an End-to-End Slim-CI-Based Production System — Creating and Testing Feature Branches and Integrating to Production |
| Managing dbt behavior with flags | §15 — Using Flags (behavior-change flags in dbt_project.yml) |
| Sub-topic | Where it's taught |
|---|---|
| Troubleshooting & managing failure points in the DAG | §22 — Production Pipelines and Artifacts, Automized Production Deployment and Development Environment Cleanup; §24 Orchestrating dbt with Dagster |
Using dbt clone |
§21 dbt in Production - Preparing a Project for Slim CI & §22 — covered within the production / state-management workflow |
| Sub-topic | Where it's taught |
|---|---|
| Generic, singular, custom, and custom generic tests | §7 Tests — Generic Tests, Singular Tests; §8 Advanced Testing — Custom Generic Tests, Custom Tests with Parameters |
| Unit tests | §7 Tests — Unit Tests |
| Testing assumptions for models & sources | §7 Tests; §13 Debugging Tests and Testing with dbt-expectations |
| Implementing testing steps in the workflow | §7 — Saving Test Failures to the Data Warehouse; §8 — Setting the Tests' Severity: Warning vs Error |
| Sub-topic | Where it's taught |
|---|---|
| Implementing dbt exposures | §11 Analyses, Hooks and Exposures — Exposures |
| Implementing source freshness | §5 Seeds and Sources — Source Freshness Checks |
| Sub-topic | Where it's taught |
|---|---|
| Understanding state and state selection | §21 dbt in Production - Preparing a Project for Slim CI — Comparing Production and Development State, Using --defer for a Production-Ready Developer Experience |
Using dbt retry |
§21 — Retrying Failed Executions - --retry |
If you'd rather scan by course section, here's the reverse view of the practical sections:
| Course section | Primary certification topic(s) |
|---|---|
| §2 Building the first version of our Project | T1 (project config) |
| §3 Models | T1 |
| §4 Materializations | T1 |
| §5 Seeds and Sources | T1, T6 |
| §6 Snapshots | T1 |
| §7 Tests | T5, T3 |
| §8 Advanced Testing: Contracts and Custom Generic Tests | T2, T5 |
| §9 Jinja, Macros and Packages | T1 |
| §10 Documentation | T1 (clean DAGs) |
| §11 Analyses, Hooks and Exposures | T1 (grants), T6 (exposures) |
| §13 Debugging Tests & dbt-expectations | T5, T3 |
| §14 Debugging with Logging | T3 |
| §15 Debugging YAML, SQL, models & General dbt Bugs | T1 (commands, --empty/--sample), T3 (flags, compiled code) |
| §16 Tags and Selectors | T1, T7 (node selection) |
| §17 Python Models | T1 |
| §18 Using Variables | T1 (supporting) |
| §19 dbt in Production - Microbatching | T1 |
| §20 dbt in Production - Model Lifecycle | T2 |
| §21 dbt in Production - Preparing for Slim CI | T4, T7 |
| §22 End-to-End Slim-CI Production System | T3, T4 |
| §24 Orchestrating dbt with Dagster | T4 |
- §1 Course Introduction, §29–§33 REFERENCE / Theory (Data Maturity Model, Data Warehouses/Lakes/Lakehouses, Modern Data Stack, Slowly Changing Dimensions, Python installation) — build the conceptual background that makes the practical topics click.
- §23 dbt Fusion and the Official VSCode Extension and §26 Power User for dbt Core — modern tooling; great for day-to-day work, lighter on the exam.
- §12 dbt Hero, §25 Capstone Project, §27 Best Practices — consolidate everything you've learned.
Head to §28 dbt Certification Exam Preparation Guide — it includes the 2026 Analytics Engineer Certification Reflection and Preparation Guide, an interview on how to prepare, and a knowledge-check sample exam. Work through it once you've reviewed the topics above, then sit a full practice test to confirm you're ready.
Good luck — you've got this! 🚀