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Updated: Jul 06, 2026
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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) All of the above-
C) The model might be based on assumptions about data that dont hold true when applied in production.
D) You might lack a clear reference and lineage back to the original data source.
2. After configuring a dbt package, you notice its models depend on a source table absent from your warehouse. How do you typically address this?
A) File an issue or submit a pull request to the package's repository.
B) Update your source definitions to include the required table.
C) Override the package model's SQL query to reference an existing table in your warehouse.
D) Rewrite the package's model from scratch within your project to remove the dependency.
3. You're setting up a new dbt project and want to customize the naming of compiled objects in the data warehouse. Which section of the dbt_project.yml file is the most relevant?
A) snapshots:
B) models:
C) seeds:
D) vars:
4. Your development environment uses a smaller-scale version of the production warehouse. You often debug issues that depend on data volume, requiring a way to temporarily "scale up" your development environment. Which strategies are feasible?
A) Run a separate dbt project against production data for testing, but target a staging schema.
B) Dynamically increase resource allocation (CPU, memory) within your data warehouse, if supported.
C) Write dbt macros that use temporary tables to simulate larger datasets during development
D) Selectively snapshot a subset of production data into your development environment.
5. (Multiple Select)
A) Avoid refactoring dbt models that are directly queried by external tools.
B) Establish a naming convention in dbt that external tools can rely upon.
C) Instruct dbt to rename columns rather than dropping and recreating them during updates.
D) Create views on top of dbt model tables, providing a stable abstraction layer for the Bl tools.
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: A,B,D | Question # 5 Answer: D |
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