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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionObjectives
Topic 1: 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
          Topic 2: dbt Core Concepts- Models and materializations
          • 1. Ref and source functions
            • 2. Views, tables, incremental models
              - Project structure and configuration
              • 1. dbt_project.yml configuration
                • 2. Packages and dependencies
                  Topic 3: 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 4: Documentation and Lineage- Data lineage understanding
                          • 1. Directed acyclic graph (DAG)
                            • 2. Dependency tracking with ref()
                              - dbt documentation system
                              • 1. Auto-generated docs site
                                • 2. Model descriptions and metadata
                                  Topic 5: Deployment and Orchestration- Running dbt in production
                                  • 1. CI/CD integration patterns
                                    • 2. dbt Cloud and job scheduling
                                      - Environments and workflows
                                      • 1. Version control with Git
                                        • 2. Development vs production environments

                                          dbt Labs dbt Analytics Engineering Certification Sample Questions:

                                          1. While setting up profiles.yml, a colleague suggests hardcoding database passwords directly. You propose alternative approaches for the sake of security. Which of the following are valid arguments in favor of your approach?

                                          A) All of the above.
                                          B) Credentials often need to be rotated, and hardcoding them would make updates cumbersome.
                                          C) Hardcoded credentials can be accidentally committed to version control, posing a security risk.
                                          D) Using environment variables or secrets managers improves maintainability and auditability of credentials.


                                          2. You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?

                                          A) Create a macro, encapsulating the date calculations, and reference it in the affected models.
                                          B) Introduce a new intermediate table to store the result of the date calculations.
                                          C) Write a custom Python function and import it into your dbt project.
                                          D) Continue copying the date calculation logic into each relevant model.


                                          3. You suspect discrepancies due to potential changes in upstream source tables. What's a practical first step to investigate?

                                          A) Review the dbt project's manifest.json for related changes.
                                          B) Compare the current state of the source tables against their historical snapshots.
                                          C) Run all dbt models and examine the logs for unusual entries.
                                          D) Contact the team responsible for managing the sources to inquire about recent updates.


                                          4. A model refactors a frequently used SQL calculation. Traditionally this logic was copied across multiple models. Which of the following is the LEAST likely benefit of refactoring this calculation into a macro?

                                          A) Simplified dependency tracking
                                          B) Faster dbt job execution time
                                          C) Reduced error potential due to a single source of logic
                                          D) Improved readability of models


                                          5. A new dataset is loaded into your warehouse as a table daily. You need to create a dbt source for it. Which is the MOST essential element to include in your configuration?

                                          A) Schema and table name.
                                          B) An enabled flag set to 'True'
                                          C) A source freshness check.
                                          D) A detailed description in the dbt_projectyml.


                                          Solutions:

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

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