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dbt Labs dbt-Analytics-Engineering exam : dbt Analytics Engineering Certification Exam

dbt-Analytics-Engineering Exam Questions
  • Exam Code: dbt-Analytics-Engineering
  • Exam Name: dbt Analytics Engineering Certification Exam
  • Updated: Oct 14, 2026
  • Q & A: 359 Questions and Answers
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

                                          dbt Labs dbt Analytics Engineering Certification Sample Questions:

                                          Question #1

                                          (Multiple Select)

                                          • A. Integrating dbt's logging with your existing monitoring and alerting platforms.
                                          • B. Require thorough code reviews with a focus on testability before deploying model changes to production.
                                          • C. Implement custom metrics tracking the duration and resource usage of individual dbt models.
                                          • D. Using dbt Cloud's built-in job monitoring and notification features, if applicable.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: A,C,D  🗳️

                                          Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

                                          Question #2

                                          You've added new tests but notice they aren't failing even though you've intentionally introduced errors. What's a LIKELY cause?

                                          • A. Your tests have incorrect severity thresholds.
                                          • B. The tests are defined in seeds.csv files.
                                          • C. Your dbt project has multiple targets configured.
                                          • D. You haven't compiled your project since writing the new tests.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: D  🗳️

                                          Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

                                          Question #3

                                          You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

                                          • A. Enforce a strict character limit on source descriptions-
                                          • B. Utilize Markdown within your descriptions to improve readability with headings and line breaks-
                                          • C. Split your source description into multiple properties (e.g., description_short and description_long)
                                          • D. Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: B,C,D  🗳️

                                          Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

                                          Question #4

                                          You need to give read-only access to a specific dataset in your production data warehouse to a data analyst on your team. How might you approach this using dbt environments?

                                          • A. Modify the data warehouse permissions directly to grant access to the required dataset.
                                          • B. Use row-level security features in your data warehouse and dynamically filter data in models using environment variables.
                                          • C. A combination of the above, depending on the capabilities of your data warehouse.
                                          • D. Create a dedicated dbt target for the analyst and utilize grants within your models to control access.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: C  🗳️

                                          Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

                                          Question #5

                                          You're setting up a dbt project with a model that generates a large intermediate result. You're concerned about performance. Which approaches could be helpful?

                                          • A. Deploy the dbt project to a smaller development environment to reduce processing overhead
                                          • B. Investigate database-specific features (e.g., temporary tables ) and refactor dbt logic to leverage them.
                                          • C. Schedule the model to run only during off-peak hours.
                                          • D. Use a staging schema for the intermediate model and adjust the job's materialization settings.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: B,D  🗳️

                                          Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).

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