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

dbt-Analytics-Engineering real exams

Exam Code: dbt-Analytics-Engineering

Exam Name: dbt Analytics Engineering Certification Exam

Updated: Nov 03, 2026

Q & A: 359 Questions and Answers

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionObjectives
dbt Core Concepts- Models and materializations
  • 1. Views, tables, incremental models
    • 2. Ref and source functions
      - Project structure and configuration
      • 1. Packages and dependencies
        • 2. dbt_project.yml configuration
          Analytics Engineering Foundations- SQL proficiency for analytics
          • 1. Data modeling in SQL
            • 2. Joins, aggregations, and window functions
              - Modern data stack concepts (ELT vs ETL)
              • 1. Role of dbt in analytics engineering
                • 2. Warehouse-centric transformation workflows
                  Deployment and Orchestration- Running dbt in production
                  • 1. CI/CD integration patterns
                    • 2. dbt Cloud and job scheduling
                      - Environments and workflows
                      • 1. Development vs production environments
                        • 2. Version control with Git
                          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
                                  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

                                          dbt Labs dbt Analytics Engineering Certification Sample Questions:

                                          Question #1

                                          (Multiple Select)

                                          • A. Indenting freshness incorrectly under the customers table.
                                          • B. The error_after freshness check syntax.
                                          • C. Misspelling "orders" as "orderes".
                                          • D. Using an 's' at the end of tables.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: A,C  🗳️

                                          Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).

                                          Question #2

                                          A model fails with an error similar to:Numeric overflow converting to data type numeric(18,2)

                                          • A. Incorrect ref() usage causing cyclical dependencies to occur-
                                          • B. Missing permissions to write data to the target table in the warehouse.
                                          • C. A timeout issue caused by the model exceeding resource limits.
                                          • D. Data within a column exceeding the precision or scale of the defined data type
                                          Reveal Solution  Discussion  0

                                          Correct Answer: D  🗳️

                                          Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).

                                          Question #3

                                          dbt Model Snippet:SQL

                                          • A. Checking that the date_part() function is compatible with your database-
                                          • B. Validating that the column names generated by the Jinja loop would be valid SQL identifiers.
                                          • C. Ensuring the grains variable is defined in dbt_project.yml or a macro.
                                          • D. Making sure the source table fct_orders contains the expected columns.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: C  🗳️

                                          Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).

                                          Question #4

                                          (Multiple Select)

                                          • A. If macros reference raw tables, those dependencies might not be reflected in dbt's lineage graphs.
                                          • B. Macros generally make dependency tracking easier by encapsulating logic.
                                          • C. Macros can completely obfuscate dependencies, making them untraceable.
                                          • D. If macros themselves have dependencies on other macros, this adds an extra layer to consider.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: A,D  🗳️

                                          Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).

                                          Question #5

                                          A colleague has introduced a new sensitive column into a source table. You need to update the dbt project documentation accordingly. Which of the following actions should you take?

                                          • A. Update the source documentation and add references to that documentation in the appropriate model's YAML file
                                          • B. Add a clear description of the sensitive column in its corresponding YAML file.
                                          • C. Add the column description and tag it using a custom sensitive tag in the YAML file.
                                          • D. Consult your organization's data governance policies and consider additional measures beyond simple documentation.
                                          Reveal Solution  Discussion  0

                                          Correct Answer: A,C,D  🗳️

                                          Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).

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