Prerequisite:

Question: Why get dbt Certified?

Answer: dbt certification offers several benefits:

• Skill Validation: It proves your proficiency in dbt concepts and best practices, with recognition directly from the dbt developers.
• Career Advancement: The certification and associated digital badge make you stand out to recruiters and potential employers seeking dbt expertise.

Question: What skills does the dbt Certification validate?

Answer: The dbt Certification confirms your ability to:

• Build and refactor data models
• Apply version control to your analytics code
• Create and implement tests for various models and data sources
• Develop and maintain clear data documentation
• Diagnose and fix errors
• Monitor data pipeline health

Question: How did the dbt Certification come about?

Answer: The dbt Certification emerged from:

• Years of observing successful dbt implementations on a global scale
• The desire to formalize the best practices that empower safe and impactful use of dbt
• The strong support of major cloud data platforms and the large dbt community

Question: What does the dbt Cloud Administration Exam evaluate? Answer: The exam tests your ability to configure, troubleshoot, optimize dbt projects, manage connections and environments, and enforce best practices for maximizing value within dbt Cloud.

Question: What are the exam logistics (duration, questions, cost, etc.)? Answer:

• Duration: 2 hours
• Number of Questions: 65
• Passing Score: 63%
• Price: $200

Question: What level of experience is recommended? Answer:

• Proficiency in SQL
• Minimum of 6+ months experience administering a dbt Cloud instance (up to version 1.4)

Question: What specific topics are covered in the exam? Answer:

• Configuring dbt Cloud data warehouse connections
• Configuring dbt Cloud git connections
• Creating and maintaining dbt Cloud environments
• Creating and maintaining job definitions
• Troubleshooting and optimizing project performance
• Configuring dbt Cloud security and licenses
• Setting up monitoring and alerting for jobs

QUESTION: What background is helpful before taking the dbt exam? ANSWER: The author recommends the following:

• SQL Fluency: 3+ years of intensive experience is ideal
• Python: At least basic understanding, even if previously used primarily for ETL.
• Git: A foundational course or equivalent knowledge.
• Analytics Engineer Role: Prior experience doing the type of work dbt facilitates will make the concepts click much faster.

QUESTION: What's the best way to study for the dbt Analytics Engineering Certification?

ANSWER: The author followed these steps:

1. dbt Fundamentals Course: Take your time, and try applying the concepts to your own sample project.
2. Official Learning Path: Work through it thoroughly, supplementing with blogs and guides.
3. Read the Docs: Seek out in-depth information on topics from the courses.
4. Specific Guides: Focus on project structure, debugging, and migration topics.
5. Relevant Blog Posts: Get insights on real-world dbt practices and review techniques.
6. Essential References: Study core configuration files (dbt_project.yml, profiles.yml, etc.) and Jinja functions until you can comfortably recall the key components.

QUESTION: How challenging is the exam, and what's the final prep like?

ANSWER: The exam requires deeper knowledge than just using dbt. Here's how the author prepped:

• Consolidate Notes: Summarize your learning into categories matching the exam's topics.
• Intense Study: Treat the final prep like serious university review.

QUESTION: What kind of topics can I expect on the exam?

ANSWER: While specific questions can't be shared, the author recalls questions related to:

• dbt Project Structure
• Source Configuration
• Jinja Functions
• Debugging dbt Projects
• Migrating from DDL/DML

FAQ:

Developing dbt models

• Identifying and verifying any raw object dependencies.
• Understanding core dbt materializations
• Conceptualizing modularity and how to incorporate DRY principles
• Converting business logic into performant SQL queries
• Using commands such as run, test, docs and seed
• Creating a logical flow of models and building clean DAGs
• Defining configurations in dbt_project.yml
• Configuring sources in dbt
• Using dbt Packages

Debugging data modeling errors

• Understanding logged error messages
• Troubleshooting using compiled code
• Troubleshooting .yml compilation errors
• Distinguishing between a pure SQL and a dbt issue that presents itself as a SQL issue
• Developing and implementing a fix and testing it prior to merging

Monitoring data pipelines

• Understanding and testing the warehouse-level implications of a model run failing at different points in the DAG
• Understanding the general landscape of tooling

Implementing dbt tests

• Using generic, singular and custom tests on a wide variety of models and sources
• Understanding assumptions specific to the datasets being generated in models and to the raw data in the warehouse
• Implementing various testing steps in the workflow
• Ensuring data is being piped into the warehouse and validating accuracy against baselines

Deploying dbt jobs

• Understanding the differences between deployment and development environments
• Configuring development and deployment environments
• Configuring the appropriate tasks, settings and triggers for the job
• Understanding how a dbt job utilizes an environment in order to build database objects and artifacts
• Using dbt commands to execute specific models

Creating and maintaining dbt documentation

• Updating dbt docs
• Implementing source, table, and column descriptions in .yml files
• Using dbt commands to generate a documentation site
• Using macros to show model and data lineage on the DAG

Promoting code through version control

• Understanding concepts and working with Git branches and functionalities
• Creating clean commits and pull requests
• Merging code to the main branch

Establishing environments in data warehouse for dbt

• Understanding environment’s connections
• Understanding the differences between production data, development data, and raw data