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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Deployment and Orchestration | - Running dbt in production
|
| dbt Core Concepts | - Project structure and configuration
|
| Documentation and Lineage | - dbt documentation system
|
| Testing and Data Quality | - Built-in and custom tests
|
| Analytics Engineering Foundations | - SQL proficiency for analytics
|
dbt Labs dbt Analytics Engineering Certification Sample Questions:
Question 1
A project stakeholder requests access to an intermediate stage of a dbt transformation pipeline to validate specific calculations.
You need to weigh this request carefully because:
A. Granting access to intermediate data may confuse stakeholders who are not familiar with the full transformation process-
B. All of the above-
C. Raw data, development data, and production data often have different levels of data quality, completeness, and timeliness.
D. Exposing intermediate results can lead to premature analysis based on incomplete data.
Question 2
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. Temporarily revert the feature branch changes, deploy the hotfix, and then restore the feature branch.
B. Merge the hotfix into the main branch and then rebase the feature branch on top of the new main branch.
C. Create a dedicated branch from the main branch specifically for the hotfix.
D. Merge the hotfix into the feature branch and wait for the entire feature to be approved.
Question 3
(Multiple Select)
A. Your data warehouse has time-based pricing tiers, and you need models to refresh during off-peak hours.
B. Upstream data sources update on a predictable cadence (e.g., daily extracts).
C. Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-
D. You need to frequently load incremental data using dbt's snapshot feature.
Question 4
You get the following error when compiling a model:ln 'models/marts/sales_report.sql':
A. Examine your model's SQL and ensure you've defined a column or combination of columns to serve as a unique identifier.
B. Add a config.get("unique_key") entry within your model configuration in dbt_project.yml.
C. Delete the existing model and create a new one with the materialization set to table.
D. Introduce a unit test to assert that the model's output has no duplicate rows.
Question 5
(Multiple Select)
A. Implement custom metrics tracking the duration and resource usage of individual dbt models.
B. Require thorough code reviews with a focus on testability before deploying model changes to production.
C. Integrating dbt's logging with your existing monitoring and alerting platforms.
D. Using dbt Cloud's built-in job monitoring and notification features, if applicable.
Solutions:
| Question 1 Answer: D | Question 2 Answer: B,C | Question 3 Answer: B,C | Question 4 Answer: A | Question 5 Answer: A,C,D |
