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Updated: Jul 16, 2026
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dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. (Multiple Select)
A) Refer to the dbt documentation for a comprehensive list of valid properties within schema files.
B) Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.
C) Utilize a YAML validator tool independently of dbt to check for general formatting errors.
D) If available, compare your schema.yml to a previous working version to spot problematic changes.
2. dbt Model Snippet:SQL
A) The source() function referencing data from the 'analytics' schema_
B) The literal use of 'analytics' as the target_database.
C) The nested subquery to calculate MAX(created_at).
D) The Jinja conditional logic used for incremental logic.
3. You write a macro to dynamically create a filter string and apply it within a model as follows:SQL
A) The generate_filter macro should be placed within a separate macros directory, not inline in the model.
B) The macro templating incorrectly nests single and double quotes.
C) You cannot dynamically apply filters using Jinja; they must be hardcoded in your model's SQL.
D) The database doesn't support comparison of the data type of the region column with a string value.
4. You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?
A) Use a custom 'accepted_values' test together with a dbt seed.
B) rely on native version history features in your data warehouse (if available).
C) Write a dbt macro to create a temporary table with historical data to compare against.
D) Use a snapshot to preserve a point-in-time reference of the column.
5. You introduce performance optimizations to a subset of models. You need to benchmark both old and new versions in an isolated environment before deployment. How might you structure your dbt commands for this comparison?
A) Within a model, conditionally write to different schemas based on an environment variable, preserving both versions.
B) Utilize dbt seed to populate separate datasets representing the old and new model outputs for side-by-side analysis.
C) Add dbt hooks to temporarily archive the original output tables before overwriting them during benchmark runs.
D) Use feature branches and switch between them with different dbt profiles for old and new code.
Solutions:
| Question # 1 Answer: A,C | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: B,C,D | Question # 5 Answer: A,D |
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