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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

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

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

2. A colleague has made updates to some model descriptions and wants you to quickly regenerate the docs to only include those changes. Which command variation would be the most efficient without a full recompilation?

A) dbt docs generate --models +[model_namel
B) Utilize dbt's incremental model feature along with dbt docs generate -no-compile.
C) dbt docs generate -select [model_name]
D) It's not possible to partially regenerate dbt docs; you need to run the full dbt docs generate command.

3. "The compiled SQL looks correct, but I'm still getting wrong results in my model!" What might you suggest to a colleague having this issue?

A) Review the logic in 'on-run-start' or 'on-run-end' hooks, as they could be modifying data-
B) Examine the source tables for data quality issues like duplicates or missing values.
C) Test the model in isolation using the dbt run -select flag to narrow down dependencies.
D) All of the above.

4. You're onboarding a new team member and need to set them up with a personal sandbox environment within your existing project. What factors do you need to consider?

A) How to provision data warehouse resources if needed, such as a separate schema or potentially even a dedicated database.
B) Whether they need access to production data replicas and how to control this securely
C) How to configure a profile on their machine and guide them to isolate changes to their sandbox.
D) All of the above.

5. (Multiple Select)

A) Check the generated column names in the compiled SQL to ensure they match existing columns in the data.
B) Run dbt Is to confirm that all dependencies for the macro and the model are resolved correctly.
C) Implement unit tests specifically for the macro to validate its output under different inputs
D) Add {{ log(macros, info=true) statements within the macro to track how it executes-

Solutions: