Going from zero to dbt certified in 4 weeks

1. Preparing for the dbt Analytics Engineering Certification Exam

Let me get started with admitting that, as with all good blog posts, I tried to make the title a little bit provocative… Yes I really had not heard from dbt before I started studying, but of course I did not actually start from zero in a broader data-ecosphere context.

In fact, the basis I started with when studying for the dbt analytics engineering certification was:

• SQL: fluency (+/- 3 years of intensive use in my day to day job); including the use of multiple “flavours” (MySQL, Oracle, Redshift,...)
• Python: +12 months of using this as an ETL tool but this got me introduced to most basic concepts of python. All in all I would say “junior” experience.
• dbt: no prior experience or knowledge
• git: I followed a 2 day GIT introduction course online before I started with dbt

Most importantly I should mention that I had several years of experience working in the role which dbt describes as the “Analytics Engineer”. As this is the role around which the whole of dbt functionality is centered; I found that most of the “problems” that dbt tries to “fix” felt very native to me and not at all abstract.

I always use the analogy of the lumberjack: for several years I was cutting down trees with my axe (=just executing .sql files manually from a workbench), and indeed I was pretty good at it! But then someone comes around and offers you a chainsaw (dbt)... Although it takes some weeks maybe to fully get the hang of how this thing works; you immediately understand how this tool will drastically change your day-to-day work for the better!

2. How to study for the dbt Analytics Engineering Certification?

With that out of the way, what I personally struggled with the most was the "blackbox" nature of the exam (it was only launched in 2022). No practice exams are available, and the "official" documentation dbt provides is pretty chaotic to be honest (docs, references, guides, blogs, tutorials,...).

Not to mention there are mountains of it and they cover insanely wide concepts, some of which seem to have nothing to do with dbt functionalities (e.g. SQL writing best-practices) but are of course crucial in becoming a true Analytics Engineer. And in the end that's what the exam tries to certify! 

All in all I spent 3-4 weeks (my eternal gratitude to my employer Biztory for letting me prepare full-time for this); roughly in the following order:

Phase 1: Started with the dbt fundamentals course.
Note that I really took my time for this (multiple days), as I assume the “5 hours” supposedly needed for the course covers only the length of the videos… I followed along with the official jaffle-shop dataset tutorial (which uses dbt Cloud and Snowflake) and then tried to make my own project in dbt Core (with a random dataset I found online and uploaded to snowflake) to make sure I thoroughly understood the basics.

Phase 2: Follow the Learning Path in the official study guide.
This takes you through all available courses, and also points you to relevant blogs, guides,… (This step took the longest!) FYI by this point I was already summarizing all relevant information from the courses into a word document (more on that later); and every subsequent step was summarized into it as well

Phase 3: Read through all available Docs.
Specifically looking for deeper and more nuanced information on the topics handled in the courses. This is also a good “recap” exercise to start consolidating the information from the courses.

Phase 4: Read and summarize a selection of relevant Guides:

• How we structure our dbt
• Debugging errors in dbt
• Debugging schema names
• Migrating from DDL, DML and stored procedures

Phase 5: Read a selection of relevant blog posts:

• Your essential dbt project checklist
• How to review an analytics pull request
• The Exact GitHub Pull Request Template we use at dbt
  

Phase 6: Read (and study!) a selection of relevant Reference pages:

• dbt_project.yml
• configs and properties
• dbt jinja functions
• source properties
• source configurations
• profiles.yml 
  

For the sixth and final point it is important to emphasize the study part, as the exam does not allow you to use anything other than what’s in your head! Broadly speaking you should be able to for example write a source.yml file by heart. Not all properties of course but the most relevant ones handled in the courses.

3. Studying... but now for real

So by the start of the fourth week this left me with a pretty big summary document to study. Note that I tried to categorize all information under the provided 8 topics on which, according to the dbt study guide, the exam focusses.

By this time I did go into full "study-like-you're-back-at-the-university" mode (bye bye Netflix binging evenings) and really tried to learn the information by heart as much as possible.

4. Taking the dbt Analytics Engineering Certification Exam

Now onto the main reason why you are here ofcourse: the Exam itself. Since a) I was hyper focused on passing the exam (and not so much trying to memorize the questions) and b) I assume it’s not allowed by dbt to share specific exam questions online; I will not be doing so.

What I can do is offer you a (non-limiting) list of generic topics on which I do remember questions being asked. This in the hope that at least it gives you some final aiming points of topics to review in those last days or even hours before you start your exam. 🙂

DISCLAIMER: The list of topics below is purely based on my own personal experience. Use it as a source of guidance, but not as your single source of truth to pass the dbt Analytics Engineering Certification Exam.

dbt Analytics Engineering Certification topics to remember list: 👇

* some common configurations:

A final tip: metrics, exposures, and python models are brand new features so as of january 2023 they were not included in the exam. Check the dbt community forums to see if this is still the case!

Good luck!