Download dbt Labs : dbt-Analytics-Engineering Questions & Answers as PDF & Test Software
Updated: Jun 03, 2026
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dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. The results of a dbt model contain values outside the expected range. You suspect an issue with Jinja templating. Where might this issue originate?
A) In the model's SQL, where you're using Jinja for calculations or dynamic logic.
B) Within a dbt_projectyml model or source configuration.
C) All of the above.
D) In a macro used by the model.
2. (Multiple Select)
A) Ensuring data synchronization after a migration.
B) Validating changes during development before deploying a new dbt model version.
C) Catching subtle errors or discrepancies that may not cause immediate functional issues.
D) Testing performance under a simulated heavy load.
3. You've included the popular 'dbt-utils' package in your project. Where should you primarily expect to find the models and macros it offers?
A) Within your 'models' directory namespaced to distinguish them from your own models.
B) Within the 'seeds' directory of your dbt project.
C) Automatically materialized as new tables in your data warehouse.
D) In the 'dbt_project.yml' file under a 'packages' section.
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) Merge the hotfix into the feature branch and wait for the entire feature to be approved.
B) Temporarily revert the feature branch changes, deploy the hotfix, and then restore the feature branch.
C) Create a dedicated branch from the main branch specifically for the hotfix.
D) Merge the hotfix into the main branch and then rebase the feature branch on top of the new main branch.
5. You need to build a model to identify customers who haven't placed orders in the past 90 days. Performance with this model is critical, as it will trigger a re-engagement campaign. Which strategy is likely to yield the best results?
A) IJse a window function to rank order dates for each customer and filter those where the most recent order is older than 90 days.
B) Create a materialized view that contains only active customers and join against the orders table.
C) Filter the orders table for dates within the past 90 days, then find customers without corresponding order IDs.
D) Join the customers and orders table, group by customer ID and use a HAVING clause to filter inactive customers.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,B,C | Question # 3 Answer: A | Question # 4 Answer: C,D | Question # 5 Answer: D |
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