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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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                                          dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q167-Q172):

                                          NEW QUESTION # 167
                                          Examine the code:
                                          select
                                          left(customers.id, 12) as customer_id,
                                          customers.name as customer_name,
                                          case when employees.employee_id is not null then true else false end as is_employee, event_signups.event_name, event_signups.event_date, sum(case when visit_accomodations.type = 'hotel' then 1 end)::boolean as booked_hotel, sum(case when visit_accomodations.type = 'car' then 1 end)::boolean as booked_ride from customers
                                          -- one customer can sign up for many events
                                          left join event_signups
                                          on left(customers.id, 12) = event_signups.customer_id
                                          -- an event signup for a single customer can have many types of accommodations booked left join visit_accomodations on event_signups.signup_id = visit_accomodations.signup_id and left(customers.id, 12) = visit_accomodations.customer_id
                                          -- an employee can be a customer
                                          left join employees
                                          on left(customers.id, 12) = employees.customer_id
                                          group by 1, 2

                                          Answer:

                                          Explanation:

                                          Explanation:
                                          Operation
                                          Correct Location
                                          Aggregations (booked_hotel, booked_ride)
                                          In a model dedicated to pre-aggregate data for reporting
                                          Standardizing customer_id
                                          In the first layer of models
                                          Selecting customer_name
                                          In the first layer of models
                                          Joining employees
                                          In a model between the first layer and final layer of models
                                          In dbt's recommended modeling framework, transformations should be centralized according to their purpose and level of abstraction. Basic cleaning and column standardization-such as shortening customers.id using left(customers.id, 12)-belongs in the first layer of models, commonly known as staging models. This layer is responsible for producing clean, consistent, analytics-ready fields. Selecting raw descriptive fields like customers.name also belongs in this layer.
                                          Joins that enrich a dataset by combining cleaned staging outputs across domains-such as joining customers with employees-belong in intermediate models. These models unify business logic that sits between the raw staging layer and the final aggregations used for reporting.
                                          Finally, aggregations like counting types of accommodations (booked_hotel and booked_ride) belong in marts or reporting models, especially when they involve summarization that downstream tools (dashboards, BI reports) will consume. This keeps heavy business logic centralized and reusable.


                                          NEW QUESTION # 168
                                          (Multiple Select)

                                          Answer: B,D

                                          Explanation:
                                          dbt seed is ideal for static data, often used in development and testing. Fact tables usually rely on upstream data sources.


                                          NEW QUESTION # 169
                                          A colleague asks, "Every time I make changes to my dbt_project.yml I have to wait for errors during dbt compile. Is there a better way?" What would you suggest?

                                          Answer: B

                                          Explanation:
                                          Linters provide real-time feedback. Schema tests are useful but don't prevent all YAML issues. Memorization and manual checks are tedious-


                                          NEW QUESTION # 170
                                          You add a new seed file. Here's a snippet of your dbt_project.yml:YAML

                                          Answer: D

                                          Explanation:
                                          column_types config applies to individual seeds, not within the general seeds section. The others might be errors in different scenarios but less likely in this case-


                                          NEW QUESTION # 171
                                          (Multiple Select)

                                          Answer: C,D

                                          Explanation:
                                          seeds are ideal for static reference data and environment-specific configurations. Large data backfills are usually handled outside of 'seeds'. Generic tests would be defined in the tests section.


                                          NEW QUESTION # 172
                                          ......

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