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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- 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
                  Documentation and Lineage- Data lineage understanding
                  • 1. Directed acyclic graph (DAG)
                    • 2. Dependency tracking with ref()
                      - dbt documentation system
                      • 1. Model descriptions and metadata
                        • 2. Auto-generated docs site
                          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
                                  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

                                          dbt Labs dbt Analytics Engineering Certification Sample Questions:

                                          1. A pull request targeting your main branch has been approved in code review but requires changes after automated tests fail. To address this, you should:

                                          A) Disable the automated tests temporarily, merge, and re-enable tests later.
                                          B) Ask the branch author to add new commits fixing the issues. Then merge when tests pass.
                                          C) Merge the pull request despite the failing tests, resolving the issues in a subsequent fix.
                                          D) Directly modify the branch by pushing commits to fix the tests.


                                          2. Data Quality Issue

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


                                          3. Your team decides to strictly enforce a linear history on the main branch. How does this decision impact your development and merge workflow?

                                          A) All of the above.
                                          B) Code reviews might need to focus on the overall change in the pull request instead of individual commits.
                                          C) Pull requests would always show a single commit upon merging with the main branch.
                                          D) Feature branches would likely need to utilize git rebase before merging.


                                          4. You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?

                                          A) Temporarily introduce a conditional branch into the macro code that is environment-aware.
                                          B) Set up a CIICD test that runs the macro in isolation using both database versions.
                                          C) Use database-specific functions to query the version information directly from within dbt models.
                                          D) Add verbose logging within the macro to track execution differences step by step.


                                          5. You want to include a multi-paragraph description for a source. Which of the following approaches is the most suitable?

                                          A) Utilize YAML's block scalar syntax (using l) to maintain formatting for multi-line descriptions.
                                          B) Reference an external Markdown file within the source description.
                                          C) dbt documentation does not support multi-paragraph descriptions for sources.
                                          D) Write a longer description directly within the YAML file, breaking it across multiple lines.


                                          Solutions:

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

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