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dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. You have complex dbt models involving incremental logic based on a timestamp column. To debug a production issue, you want to temporarily force a full rebuild of a specific incremental model. Which dbt command-line options could help?
A) Write a macro within the model to switch behavior based on an environment variable.
B) Manually update the timestamp column in the incremental model's output table.
C) Use dbt artifacts or the compilation context to access the current run's timestamp and override model logic.
D) Add the -full-refresh flag to the dbt run command.
2. You have a time-sensitive incremental model that must run as quickly as possible whenever new data arrives. What dbt feature or configuration might be crucial for optimization?
A) Set the model to refresh on a frequent, time-based schedule.
B) Use the dbt run -select flag to execute only that specific model.
C) Configure the model to run with maximum possible concurrency.
D) Define appropriate materializations (table, view, incremental) for the model.
3. A project stakeholder prefers a text-based lineage representation over the graphical DAG. Which of these approaches might be a suitable alternative?
A) Disable the DAG visualization entirely within the generated documentation.
B) Extend the dbt documentation framework to create a custom text-based lineage view
C) Utilize a macro that generates a Markdown list representation of model dependencies.
D) Rely on third-party tools that specialize in parsing dbt projects to produce textual lineage outputs
4. Your team sometimes defines raw data sources directly in dbt models using .yml files within the models directory. When might this approach be problematic?
A) If the raw dataset requires complex transformations.
B) If the raw dataset is accessed via a non-standard data warehouse connector.
C) If the raw dataset's schema is subject to change.
D) If multiple models utilize the same raw dataset.
5. (Multiple Select)
A) Populating a lookup table with country codes.
B) Inserting large volumes of historical transactional data for backfilling purposes.
C) Loading configuration data for production environments that differs from development settings.
D) Defining generic tests that apply to multiple data sources.
Solutions:
| Question # 1 Answer: C,D | Question # 2 Answer: B,D | Question # 3 Answer: B,C,D | Question # 4 Answer: D | Question # 5 Answer: A,C |





