Updated: Jul 12, 2026
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1. The dataset you work with contains personally identifiable information (PII). Which types of additional tooling might be necessary for your project beyond a core dbt implementation?
A) Advanced statistical modeling tools.
B) Enhanced data access governance and auditing tools.
C) Data masking or tokenization solutions.
D) Automated ETL pipelines
2. (Multiple Select)
A) materialized: view
B) test-paths
C) model-paths
D) database
3. (Multiple Select)
A) Using clear and consistent naming conventions across models and macros.
B) Encapsulating complex logic in well-named macros with descriptive comments.
C) Minimizing explicit model dependencies to create loosely coupled components.
D) Preferring long, monolithic SQL files with minimal CTEs.
4. You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?
A) In the 'dbt_project.yml' file under a 'packages' section.
B) Within the 'seeds' directory of your dbt project.
C) Within your 'models' directory namespaced to distinguish them from your own models.
D) Automatically materialized as new tables in your data warehouse.
5. You need a way to document relationships between raw source tables, dbt models, and downstream Bl dashboards. What type of tool would be best suited for this task?
A) A data catalog
B) A version control system
C) dbt's built-in documentation (dbt docs)
D) A data profiling tool
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: B,C | Question # 3 Answer: A,B | Question # 4 Answer: C | Question # 5 Answer: A |
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