How to pass dbt Analytics Engineering Certification Exam
Welcome to the final article in the Understanding dbt series!
If you’ve followed along from the beginning, we’ve explored almost every corner of dbt: from building models and managing snapshots to testing, documenting, and even monitoring your projects. Now, it’s time to bring it all together and talk about something many data professionals aim for — the dbt Analytics Engineering Certification.
Why bother with a certification?
Because it’s more than just a badge. It’s a signal that you understand the core practices of modern data transformation and that you’re ready to take on real-world analytics engineering work.
In this article, I’ll walk you through what to expect from the exam, how to prepare effectively, and some practical tips to help you feel confident on test day.
Let’s get started.
What the dbt Certification Covers
The dbt Analytics Engineering Certification isn’t just a checkbox for your resume — it’s a way to validate that you know how to use dbt the way it’s used in production: confidently, cleanly, and collaboratively. The exam covers a broad set of topics across model development, testing, debugging, documentation, and governance.
Let’s walk through what you need to know — and link to the right resources to review each topic properly.
Core Modeling Skills
This is the bread and butter of dbt. You’re expected to:
Develop models using SELECT statements that transform raw data into usable datasets.
Understand upstream dependencies, i.e., how sources and staging layers feed into marts and dashboards.
Use modularity to break transformations into logical, reusable pieces. Think: stg_, int_, and dim_ models.
Create a clean DAG (Directed Acyclic Graph) by thoughtfully organizing models and using proper naming conventions.
You should also know how materializations affect performance and when to use a view, table, or incremental.
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Working with Configurations
In real-world projects, your dbt setup is rarely plug-and-play. You’ll be tested on your ability to configure dbt properly:
Defining behavior in dbt_project.yml like materialization defaults, model paths, and custom configurations.
Setting up sources with freshness criteria and descriptions.
Granting access to downstream users using the grants configuration block.
Using dbt packages, both official and custom, to reuse logic or tools across teams.
Seed files — static CSVs stored in your project — are also covered. You need to know how to load, configure, and use them in joins.
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Testing & Debugging
Testing is at the core of dbt’s “trust but verify” approach.
Generic tests like unique, not_null, and accepted_values should be second nature.
You’ll also need to write custom tests — both singular (with your own logic) and generic (reusable across models).
Understand how tests fail and what error messages mean.
Know how to troubleshoot model errors, including checking the compiled SQL and warehouse logs.
This section of the exam validates whether you can confidently maintain quality in a production pipeline.
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Documentation & Governance
Clean code is great , but well-documented and governed code is essential.
Know how to document sources, models, columns, and tests in .yml files.
Understand how to generate and serve dbt docs, including lineage and DAG visibility.
Use contracts to lock down the shape of your models, ensuring downstream consistency.
Understand versioning and deprecation, which help teams evolve models over time.
Know how to use exposures to track dashboard dependencies and define what’s important.
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Workflow Automation & Extension
Finally, the exam touches on enhancing and extending your dbt workflow:
Analyses are for running ad hoc or repeatable queries (like metrics or sanity checks).
Hooks allow you to plug into model execution and run custom logic before or after models — e.g., row counts, audit logging, or alerting.
Sources and seeds are also part of the pipeline and need to be well-integrated.
The idea is that you can manage more than models — you can own the pipeline end-to-end.
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How the Exam Works
Now that you know what to expect in terms of content, let’s talk about the exam itself — the format, structure, logistics, and how to prepare effectively.
Question Types
The dbt Analytics Engineering Certification exam includes a mix of interactive and conceptual questions. This isn’t just about knowing definitions — you’ll need to think through scenarios, troubleshoot issues, and apply best practices. The question types include:
Multiple Choice — Choose the one correct answer
Multiple Select — Select all correct answers
Fill in the Blank — Type your answer (e.g., a command or config)
Matching — Connect related items like materializations to behavior
Hotspot — Click on the correct area in a dbt file or DAG diagram
Build List — Reorder items in logical flow (like model dependencies)
DOMC (Discrete Option Multiple Choice) — Options appear one at a time, and you confirm whether each one is correct
This variety keeps the test engaging, but it also means you need real understanding, not just memorization.
You can register and take the exam from anywhere — just make sure you’re in a quiet environment with a reliable internet connection.
Official Study Guide
Before anything else, we highly recommend starting with the official dbt study guide. It breaks down everything you’ll be tested on, links to documentation, and includes sample questions.
If you read only one thing before your exam, make it that guide.
Practice Makes Prepared: Try Our Udemy Tests
To help you go from theory to confidence, we created a set of practice tests on Udemy that helps fill the gaps in your knowledge and simulate exam-style thinking.
You’ll find:
300+ questions across all certification domains
Scenarios that challenge your understanding
Instant feedback and explanations for every answer
We built these tests because we wish they existed when we were preparing. They’re your training ground to go into the real exam calm, confident, and fully prepared and also by using link above it will be free for you!
Final Thoughts and Additional Resources
When I first started working with dbt, I faced the same challenge many people do — there wasn’t a single, beginner-friendly place that explained everything clearly. One of the things that really helped me get from zero to hero was a comprehensive Udemy course.
It didn’t just cover dbt in theory — it walked through how to use it in practice, step by step. It wasn’t about passing the certification, but about really understanding how dbt works in real projects.
I highly recommend taking a course like that if you want to gain a strong, practical foundation.
In addition, the official dbt documentation was incredibly helpful once I got more comfortable. It’s detailed, well-maintained, and a must-have resource for deeper insights.
That said, I found that there still wasn’t enough structured, clear guidance for people learning on their own — so I started this free article series to fill that gap and help others like me.
If you found this article helpful, here’s how you can keep going:
Check out my Udemy dbt Certification Practice Tests — they’re not about memorizing real questions, but about filling knowledge gaps and feeling confident before the exam
Follow me so you don’t miss the next posts, tutorials, and resources I publish around dbt and data engineering
Thanks for reading — and good luck on your certification journey!
You’ve got this. 🚀