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Last Updated: Sep 11, 2026
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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Models | 25% | - Materializations (table, view, ephemeral, incremental) - Sources and references - Seeds - Writing and managing SQL models - Snapshots |
| Deployment and Orchestration | 15% | - Environments (dev, staging, prod) - CI/CD with dbt Cloud - Git version control integration - Jobs and scheduling in dbt Cloud |
| Data Transformation Techniques | 25% | - Macros and packages - Jinja templating - Common table expressions and subqueries - Refactoring and incremental models |
| Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests - Documentation generation |
| dbt Fundamentals | 15% | - dbt workflow and best practices - dbt Core vs dbt Cloud - dbt project structure |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Your dbt project contains models that process data with varying levels of sensitivity. Which of the following might be reasons to utilize different schemas or even different targets within your data warehouse for these data sensitivity levels?
- A. All of the above-
- B. To implement stricter access controls limiting which users or roles can interact with sensitive data-
- C. To comply with data governance regulations that mandate separation of different data types.
- D. To optimize query performance, as certain models might benefit from specific data warehouse configurations based on their workload.
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You've defined a model as follows:SQL
- A. A data type mismatch between a column in 'stg_customers' and how the query is using it for the join.
- B. A Jinja templating error: Misusing the source() function within the count() function.
- C. An incorrect materialization strategy causing the wrong data to be populated in a downstream source.
- D. A pure SQL issue: The count() function isn't supported for the data type of a column in the 'cust_orders' source.
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(Multiple Select)
- A. test-paths
- B. model-paths
- C. database
- D. materialized: view
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You're working with a dbt project in both development and production environments.
Which configuration technique is best suited to handle differences in database connection details between these environments?
- A. Create separate branches for each environment and maintain different dbt_project.yml files in each branch.
- B. Utilize environment variables in conjunction with the profiles.yml file.
- C. Use the target flag when running dbt commands.
- D. Define multiple profile sections within the dbt_project.yml
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A downstream process relies on a critical model to be fully refreshed daily. How can you enforce this behavior using dbt_project.yml configuration?
- A. Update the model configuration to materialized: table and add a persist_docs: true property.
- B. Set the model's materialized property to incremental _ refresh-
- C. Apply a post-hook that triggers a full refresh of the model.
- D. Configure a schedule property within the models: section.
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