Beginner

Certification Overview

Everything you need to know about the dbt Analytics Engineering certification — exam format, domains, cost, prerequisites, study plans, and the registration process.

Exam At a Glance

The dbt Analytics Engineering certification validates your ability to build, test, document, and deploy data transformation pipelines using dbt. It covers dbt fundamentals, testing and documentation, advanced modeling patterns, and production deployment best practices.

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Key facts: 65 questions • 90 minutes • Multiple choice • $200 USD • Valid for 2 years • Online proctored

Question Format

The exam consists of two question types:

  • Multiple choice — One correct answer out of four options (majority of questions)
  • Multiple select — Two or more correct answers (clearly marked as "select ALL that apply")

There is no penalty for guessing. Never leave a question blank. If you are unsure, eliminate what you can and make your best choice.

Scoring

The exam uses a pass/fail scoring model. You need approximately 65-70% of questions correct to pass. The exact threshold may vary. Some questions may be unscored pilot questions used for future exam development.

Exam Domains

The exam is organized around four core domains. Understanding these domains and their relative weights is critical for prioritizing your study time.

Domain 1: dbt Fundamentals

Models, sources, refs, seeds, materializations (table, view, incremental, ephemeral), project structure, profiles, and dbt Cloud vs. dbt Core.

Domain 2: Testing & Documentation

Schema tests (unique, not_null, accepted_values, relationships), custom tests, data tests, documentation blocks, and dbt packages.

Domain 3: Advanced Modeling

Incremental models, snapshots (SCD Type 2), macros, Jinja templating, hooks, tags, exposures, and advanced project configurations.

Domain 4: Deployment & Operations

dbt Cloud jobs, CI/CD pipelines, environment management, manifest artifacts, source freshness, and production best practices.

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Study priority: dbt Fundamentals and Testing are heavily tested. If you are short on time, prioritize hands-on experience with models, refs, materializations, schema tests, and incremental models.

Prerequisites

dbt Labs recommends the following background before attempting the exam:

  • SQL proficiency — Strong understanding of SELECT, JOIN, CTEs, window functions, and aggregations
  • 6+ months of experience using dbt for data transformation
  • Completion of the free dbt Fundamentals course on dbt Learn
  • Familiarity with Git basics (branching, pull requests, version control)
  • Understanding of data warehouse concepts (Snowflake, BigQuery, Redshift, or Databricks)

4-Week Accelerated Study Plan

For experienced dbt practitioners who want to move quickly:

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Week 1: dbt fundamentals deep dive — models, sources, refs, materializations, seeds, project structure
Week 2: Testing and documentation — schema tests, custom tests, data tests, packages
Week 3: Advanced modeling — incremental models, snapshots, macros, Jinja, hooks
Week 4: dbt for ML + deployment + practice exam + review weak areas

Registration Process

  1. Create or sign in to your dbt Labs account
  2. Navigate to the Certifications section on the dbt website
  3. Select dbt Analytics Engineering Certification
  4. Schedule your exam through the online proctoring platform
  5. Pay the $200 USD exam fee
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Pro tip: Complete the free dbt Fundamentals course on dbt Learn before scheduling your exam. It covers many of the core concepts tested and provides hands-on experience with a real dbt project.

What Is Next

Now that you understand the exam format and have a study plan, it is time to dive into the first domain. In the next lesson, we cover dbt Fundamentals — models, sources, refs, and materializations, with practice questions to test your knowledge.