dbt Labs dbt Analytics Engineering Certification : dbt-Analytics-Engineering

  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
  • Updated: Aug 22, 2026
  • Q&As: 359 Questions and Answers

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionObjectives
Deployment and Orchestration- Running dbt in production
  • 1. dbt Cloud and job scheduling
    • 2. CI/CD integration patterns
      - Environments and workflows
      • 1. Development vs production environments
        • 2. Version control with Git
          dbt Core Concepts- Project structure and configuration
          • 1. dbt_project.yml configuration
            • 2. Packages and dependencies
              - Models and materializations
              • 1. Ref and source functions
                • 2. Views, tables, incremental models
                  Analytics Engineering Foundations- SQL proficiency for analytics
                  • 1. Joins, aggregations, and window functions
                    • 2. Data modeling in SQL
                      - Modern data stack concepts (ELT vs ETL)
                      • 1. Warehouse-centric transformation workflows
                        • 2. Role of dbt in analytics engineering
                          Testing and Data Quality- Built-in and custom tests
                          • 1. Generic tests (unique, not null, relationships)
                            • 2. Custom SQL tests
                              - Data validation strategies
                              • 1. Schema testing practices
                                • 2. CI-based validation workflows
                                  Documentation and Lineage- dbt documentation system
                                  • 1. Model descriptions and metadata
                                    • 2. Auto-generated docs site
                                      - Data lineage understanding
                                      • 1. Directed acyclic graph (DAG)
                                        • 2. Dependency tracking with ref()

                                          dbt Labs dbt Analytics Engineering Certification Sample Questions:

                                          1. (Multiple Select)

                                          A) Enable version control on your dbt_project.yml file and related configuration.
                                          B) Integrate a specialized data lineage tool that tracks dependencies down to the database level..
                                          C) Configure dbt to log all executed SQL statements with sufficient metadata for auditing.
                                          D) Leverage your data warehouse's built-in auditing or change tracking features, if available.


                                          2. (Multiple Select)

                                          A) A ref function referencing another model is incorrect.
                                          B) You made a typo when defining the column name in your model.
                                          C) An upstream source table schema was updated without updating the dbt model.
                                          D) Your dbt tests are invalid.


                                          3. You're integrating a new API-based data source. Select the most suitable way to incorporate this into your dbt project.

                                          A) Create a source with a loaded_at_field and write custom logic to snapshot API data.
                                          B) Create a seed file to load the data directly into a table.
                                          C) Utilize a dbt package tailored for the specific API, if available.
                                          D) Write a dbt macro to encapsulate the API interaction logic.


                                          4. Data Quality Issue

                                          A) Run dbt compile followed by dbt run -select model_name to isolate the model and observe its output closely.
                                          B) Inspect any filters or transformations within the model's SQL that might be unintentionally excluding data.
                                          C) Add specific schema tests to the model, asserting that the column in question should not contain nulls.
                                          D) Run dbt test on the model and see if existing tests fail, indicating a possible regression.


                                          5. You run a dbt job that heavily utilizes CT Es (Common Table Expressions). In your development environment, it works flawlessly. However, in production (a different database technology), it throws errors. Which potential causes should you investigate?

                                          A) This indicates a bug in dbt's cross-database compatibility layer.
                                          B) Your production environment has resource constraints that impact the execution of complex queries.
                                          C) The production database has limitations or different syntax conventions related to CTEs.
                                          D) CTE performance optimizations are automatically applied in development, but not in production.


                                          Solutions:

                                          Question # 1
                                          Answer: A,B,C,D
                                          Question # 2
                                          Answer: A,B,C
                                          Question # 3
                                          Answer: C
                                          Question # 4
                                          Answer: A,B,C,D
                                          Question # 5
                                          Answer: B,C

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