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dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. You need a very niche data transformation that no existing packages address. When should you consider building your own dbt package?

A) Only after you have extensively searched for potential alternative solutions.
B) If the transformation logic is complex and likely to be valuable to others in the dbt community.
C) When strict versioning and control of the functionality are critical for your project
D) Whenever you need to encapsulate custom SQL logic for reuse across models.

2. (Multiple Select)

A) Using an 's' at the end of tables.
B) Indenting freshness incorrectly under the customers table.
C) The error_after freshness check syntax.
D) Misspelling "orders" as "orderes".

3. The dataset raw user activity is critical to several models. You've been informed that a major schema change is scheduled for this source. Select the best way to proactively mitigate the potential impact of this change on your dbt project.

A) Create custom data tests that validate the critical columns in raw_user_activity.
B) Pause all dbt jobs until the schema change risk is fully assessed.
C) Communicate with the relevant data team to create a temporary staging table mirroring the old schema.
D) Run dbt test after the schema changes and address any failures.

4. You have an urgent hotfix that needs to be deployed while a larger feature branch is still under review. Which strategies could you consider?

A) Create a dedicated branch from the main branch specifically for the hotfix.
B) Merge the hotfix into the feature branch and wait for the entire feature to be approved.
C) Merge the hotfix into the main branch and then rebase the feature branch on top of the new main branch.
D) Temporarily revert the feature branch changes, deploy the hotfix, and then restore the feature branch.

5. You write a macro to dynamically create a filter string and apply it within a model as follows:SQL

A) The database doesn't support comparison of the data type of the region column with a string value.
B) You cannot dynamically apply filters using Jinja; they must be hardcoded in your model's SQL.
C) The generate_filter macro should be placed within a separate macros directory, not inline in the model.
D) The macro templating incorrectly nests single and double quotes.

Solutions: