DBT (Data Build Tools) Fundamentais

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dbt (Data Build Tool): dbt Fundamental Training Course : To access the same please login and enroll.



dbt (Data Build Tool): dbt Fundamental Training Course : To access the same please login and enroll.


+ Requirements & FAQ


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



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

dbt (Data Build Tool): dbt Fundamental Training Course : To access the same please login and enroll.



dbt (Data Build Tool): dbt Fundamental Training Course : To access the same please login and enroll.


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