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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?

A) Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
B) Add a depends_on relationship pointing to the potentially late table within your model.
C) Configure a dbt hook to run before the job, dynamically checking freshness of the source table.
D) Set the -full-refresh flag for the dbt job to ensure all tables are materialized.

2. "Isn't debugging harder with many smaller models in a DAG?" How would you address this concern expressed by a colleague?

A) "Smaller models are easier to test individually, leading to faster issue identification."
B) "We'll rely extensively on snapshots to trace errors back to their origin."
C) "Our DAG will be simple, so debugging model dependencies won't be a concern."
D) "Debugging is slightly harder, but the benefits outweigh the added effort."

3. Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?

A) Utilize environment variables to store seed data values.
B) Employ a secrets management tool and integrate it with your dbt deployment.
C) Create separate seed files and add them to your .gitignore or equivalent.
D) Write the seed data into log files, excluding them from version control.

4. 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?

A) dbt deps followed by dbt compile
B) dbt seed to populate any new source tables.
C) dbt run followed by dbt test on both the feature branch and the main branch.
D) dbt docs generate on the new feature branch.

5. Business requirements call for a report ranking products based on year-over-year (YoY) revenue growth. Historical data must be preserved. What's a potential issue in implementing this directly in a dbt model?

A) ref() functions can hinder performance when working with historical data.
B) Data warehouses might not be optimized for period-over-period comparisons.
C) Views cannot perform complex date manipulation required for YoY calculations.
D) Incremental materializations aren't suitable for trend analysis.

Solutions: