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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionObjectives
Topic 1: 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 2: Analytics Engineering Foundations- Modern data stack concepts (ELT vs ETL)
          • 1. Role of dbt in analytics engineering
            • 2. Warehouse-centric transformation workflows
              - SQL proficiency for analytics
              • 1. Data modeling in SQL
                • 2. Joins, aggregations, and window functions
                  Topic 3: Deployment and Orchestration- Environments and workflows
                  • 1. Development vs production environments
                    • 2. Version control with Git
                      - Running dbt in production
                      • 1. dbt Cloud and job scheduling
                        • 2. CI/CD integration patterns
                          Topic 4: Testing and Data Quality- Data validation strategies
                          • 1. CI-based validation workflows
                            • 2. Schema testing practices
                              - Built-in and custom tests
                              • 1. Generic tests (unique, not null, relationships)
                                • 2. Custom SQL tests
                                  Topic 5: dbt Core Concepts- Project structure and configuration
                                  • 1. Packages and dependencies
                                    • 2. dbt_project.yml configuration
                                      - Models and materializations
                                      • 1. Views, tables, incremental models
                                        • 2. Ref and source functions

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                                          최신 Analytics Engineers dbt-Analytics-Engineering 무료샘플문제 (Q308-Q313):

                                          질문 # 308
                                          A critical model fails with a non-descriptive error. You suspect a third-party package used in the model. How might you narrow down the problem?

                                          정답：D

                                          설명：
                                          Start by understanding if the problem lies within the package itself. Disabling or isolating package models can help. Structured logging is useful for analysis.


                                          질문 # 309
                                          You discover a downstream model, dim products, references an outdated raw dataset, raw_product_archive, intended for historical analysis and not active products. What's the most appropriate corrective action?

                                          정답：A

                                          설명：
                                          Directly addressing the root cause ensures your model uses the correct source. Schema tests are safeguards but don't resolve the underlying dependency issue. Be cautious about deleting sources unless there's a very strong reason.


                                          질문 # 310
                                          You're collaborating with others on a dbt project. A new feature branch introduces unexpected errors. Which command combination helps analyze potential issues before merging into the main branch?

                                          정답：D

                                          설명：
                                          Running models and tests on both branches aids comparison and debugging. While 'docs generate' is helpful, it isn't sufficient for pinpointing runtime errors.


                                          질문 # 311
                                          You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?

                                          정답：A,B,C,D

                                          설명：
                                          Each scenario could cause a baseline comparison test to fail, covering issues in the data pipeline, the baseline, and the test logic itself.


                                          질문 # 312
                                          A newly-hired analyst needs to update a downstream model that depends on a chain of intermediate models. To minimize the potential for disruption, how can modularity help them?

                                          정답：C

                                          설명：
                                          Modularity and well-defined dependencies make it easier to isolate the impact of a change, simplifying updates and testing.


                                          질문 # 313
                                          ......

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                                          dbt-Analytics-Engineering최신 덤프공부자료: https://www.itexamdump.com/dbt-Analytics-Engineering.html

                                          2026 Itexamdump 최신 dbt-Analytics-Engineering PDF 버전 시험 문제집과 dbt-Analytics-Engineering 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1t3KiIhPcydy85gMmfDtDPioHJ0G4sDMg