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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A new dbt model is created directly in the development environment without a corresponding raw data source. Which of these can be potential ramifications of this approach?

A) There might be limited ability to reproduce or troubleshoot the model if the development data is modified or deleted.
B) The model might be based on assumptions about data that dont hold true when applied in production.
C) You might lack a clear reference and lineage back to the original data source.
D) All of the above-

2. While working on a feature branch, you need to incorporate some critical bug fixes that were merged into the main branch. Which Git commands, or combination of commands, would you use?

A) git checkout main followed by git pull
B) git merge main
C) Both B and C are viable options.
D) git rebase main

3. A sudden data quality issue occurs in production. You need to quickly reproduce the problem in a lower environment but retain a point-in-time snapshot of production dat a. Which strategies might you employ? Create a backup of the production database and restore it to a separate development or testing database.

A) A combination of the above, depending on time constraints and available resources.
B) Utilize data replication features (if supported by your data warehouse) to create a near real-time mirror of production data in a
C) Use dbt snapshots to capture critical production tables during a defined timeframe for later use in troubleshooting.
D) staging area.

4. A colleague asks, "Why do we need dbt docs when we already have good database documentation?" Provide a compelling response.

A) dbt docs primarily serves as a replacement for dbt seed.
B) dbt docs is mandatory for version control with dbt projects.
C) dbt docs automatically tracks model testing results.
D) dbt docs generates dynamic documentation that directly reflects your project's dependencies and current state.

5. Your dbt project contains models that process data with varying levels of sensitivity. Which of the following might be reasons to utilize different schemas or even different targets within your data warehouse for these data sensitivity levels?

A) To implement stricter access controls limiting which users or roles can interact with sensitive data-
B) To optimize query performance, as certain models might benefit from specific data warehouse configurations based on their workload.
C) To comply with data governance regulations that mandate separation of different data types.
D) All of the above-

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