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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

SectionObjectives
Topic 1: 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
          Topic 2: Documentation and Lineage- dbt documentation system
          • 1. Auto-generated docs site
            • 2. Model descriptions and metadata
              - Data lineage understanding
              • 1. Dependency tracking with ref()
                • 2. Directed acyclic graph (DAG)
                  Topic 3: 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. Warehouse-centric transformation workflows
                        • 2. Role of dbt in analytics engineering
                          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. Schema testing practices
                                • 2. CI-based validation workflows
                                  Topic 5: Deployment and Orchestration- Environments and workflows
                                  • 1. Development vs production environments
                                    • 2. Version control with Git
                                      - Running dbt in production
                                      • 1. CI/CD integration patterns
                                        • 2. dbt Cloud and job scheduling

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                                          dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q141-Q146):

                                          NEW QUESTION # 141
                                          A critical model depends on a third-party data source with periodic update delays. How could you structure your DAG to mitigate the impact of these delays on downstream reporting?

                                          Answer: C

                                          Explanation:
                                          The 'defer' option lets your DAG continue partially while awaiting the delayed data. Others offer solutions but don't address the core DAG flow issue.


                                          NEW QUESTION # 142
                                          Examine the configuration for the source:
                                          sources:
                                          - name: jaffle_shop
                                          schema: jaffle_shop_raw_current
                                          tables:
                                          - name: orders
                                          identifier: customer_orders
                                          Which reference to the source is correct?

                                          Answer: C

                                          Explanation:
                                          In dbt, the source() function resolves a source by its declared source name and table name, not by the physical schema or identifier in the warehouse. The YAML block defines a source named jaffle_shop, and under that source, a table named orders. The identifier: customer_orders field tells dbt that although the logical table name is orders, the actual physical object in the warehouse is named customer_orders.
                                          dbt always expects the syntax:
                                          {{ source(source_name, table_name) }}
                                          Here, the correct reference uses jaffle_shop as the source name and orders as the table name because these are the logical names assigned in the YAML. dbt internally resolves the physical table name via the identifier field, so the model should not reference customer_orders directly.
                                          Option A and B are incorrect because the first argument is not the schema; dbt does not use schemas in the source() call. Option D is incorrect because customer_orders is the warehouse identifier, not the logical table name recognized by dbt.
                                          Therefore, the correct reference is:
                                          {{ source('jaffle_shop', 'orders') }}
                                          This ensures consistent modeling, dependency tracking, and accurate documentation.


                                          NEW QUESTION # 143

                                          Answer:

                                          Explanation:

                                          Explanation:
                                          (new_project)
                                          (models:)
                                          (grants)
                                          (require-dbt-version: '>= 1.0.0' )
                                          The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores.
                                          Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt's project naming rules.
                                          To configure how models behave-materializations, schemas, tags, or per-directory overrides-dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.
                                          When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.
                                          Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.


                                          NEW QUESTION # 144
                                          You suspect discrepancies due to potential changes in upstream source tables. What's a practical first step to investigate?

                                          Answer: D

                                          Explanation:
                                          Understanding if there were any intentional data source changes is crucial before diving into technical troubleshooting. Options A, B, and D might be necessary subsequent steps.


                                          NEW QUESTION # 145
                                          You need a very niche data transformation that no existing packages address. When should you consider building your own dbt package?

                                          Answer: C

                                          Explanation:
                                          The primary benefit of creating a custom package lies in sharing it with others. If it's solely for your project, macros or well-structured models might suffice


                                          NEW QUESTION # 146
                                          ......

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