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

dbt-Analytics-Engineering pass collection

Exam Code: dbt-Analytics-Engineering

Exam Name: dbt Analytics Engineering Certification Exam

Updated: Aug 18, 2026

Q & A: 359 Questions and Answers

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionObjectives
Topic 1: Documentation and Lineage- dbt documentation system
  • 1. Auto-generated docs site
    • 2. Model descriptions and metadata
      - Data lineage understanding
      • 1. Directed acyclic graph (DAG)
        • 2. Dependency tracking with ref()
          Topic 2: dbt Core Concepts- Models and materializations
          • 1. Views, tables, incremental models
            • 2. Ref and source functions
              - Project structure and configuration
              • 1. dbt_project.yml configuration
                • 2. Packages and dependencies
                  Topic 3: Deployment and Orchestration- Environments and workflows
                  • 1. Version control with Git
                    • 2. Development vs production environments
                      - Running dbt in production
                      • 1. dbt Cloud and job scheduling
                        • 2. CI/CD integration patterns
                          Topic 4: 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
                                  Topic 5: Testing and Data Quality- Built-in and custom tests
                                  • 1. Custom SQL tests
                                    • 2. Generic tests (unique, not null, relationships)
                                      - Data validation strategies
                                      • 1. Schema testing practices
                                        • 2. CI-based validation workflows

                                          dbt Labs dbt Analytics Engineering Certification Sample Questions:

                                          1. After adding several new models to your project, you want to execute only those models and their direct dependencies. Which command combination best achieves this?

                                          A) dbt build
                                          B) dbt run followed by dbt test
                                          C) dbt run -exclude model namel model name2
                                          D) dbt run -models +model namel +model name2 ...


                                          2. Type Mismatch

                                          A) Use a CAST operation within the affected model's SQL, leaving the underlying source unchanged.
                                          B) Modify the source table directly in the database to adjust the data type, ensuring backward compatibility.
                                          C) Handle the type conversion with a custom macro and apply it selectively within your dbt project.
                                          D) Introduce a staging model specifically to perform the type conversion, creating a clean separation.


                                          3. You need to roll out a database migration mid-month. It requires changes to some of your dbt model schemas. How would you manage a smooth transition?

                                          A) Use feature branches and environment-aware logic in dbt to allow pre- and post-migration versions to coexist temporarily.
                                          B) Duplicate models, modifying them for the new schema, and switch over in your job configuration.
                                          C) Pause all dbt jobs until after the database migration is fully completed.
                                          D) Deploy model changes alongside database changes, accepting potential errors for a period.


                                          4. (Multiple Select)

                                          A) Upstream data sources update on a predictable cadence (e.g., daily extracts).
                                          B) Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-
                                          C) You need to frequently load incremental data using dbt's snapshot feature.
                                          D) Your data warehouse has time-based pricing tiers, and you need models to refresh during off-peak hours.


                                          5. (Multiple Select)

                                          A) Apply an incremental materialization strategy to subsequent models if appropriate.
                                          B) Use CTEs or views in the source definition to pre-filter columns or apply initial transformations.
                                          C) Introduce a source snapshot to create a point-in-time copy of the data.
                                          D) Increase the query_comment timeout setting in your dbt_project. yml


                                          Solutions:

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

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