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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| dbt Models Governance | 15% | - Project organization and structure - Naming conventions and standards - Version control integration |
| External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Implementing dbt Tests | 10% | - Test configuration and execution - Custom tests - Built-in tests |
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Identifying modeling errors - Debugging techniques |
| Developing dbt Models | 20% | - Model optimization
|
| Managing Data Pipelines | 15% | - Deployment strategies - Pipeline orchestration - CI/CD integration |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| Creating and Maintaining Documentation | 10% | - Generating documentation - Descriptions and metadata - Documentation standards |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
1. You have a dbt model that performs complex aggregations. During development, you notice some models take excessively long to run, but they ultimately complete without errors. Which of the following might be true?
A) These models likely have issues with joins or CTE structure impacting performance.
B) This behavior automatically triggers an optimization feature within dbt.
C) The models are at risk of failing if the source data grows substantially.
D) Downstream models will inherit the performance issues.
2. You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?
A) Creating multiple deployment environments in dbt Cloud.
B) dbt profiles defined in your dbt_project.yml file.
C) dbt's -vars flag to override variable values.
D) Using dbt tags to selectively execute parts of your project.
3. You have a computationally expensive dbt model that creates a wide table with many columns. You realize that downstream analysis typically only needs a small subset of these columns. How might you optimize this for both
A) Implement a post-hook that immediately deletes unused columns from the model's output table.
B) Refactor the model to use a view materialization, selecting only necessary columns.
C) Configure a dbt job to run as a lower priority task, accepting slower processing.
D) Increase memory and CPU resources allocated to the data warehouse to power through the large build-
4. After merging a large pull request, you notice a critical issue introduced in one of the commits. What actions might be necessary?
A) All of the above could be needed as part of the fix.
B) Use git revert to create a new commit that specifically undoes the problematic changes.
C) Identify the problematic commit using git log or git blame.
D) Use git cherry-pick to apply specific commits from the reverted change to another branch, effectively restoring the necessary parts.
5. You want to automatically deploy updates to your documentation site hosted on a cloud service whenever changes to your dbt project are committed to your main branch. Which of the following is essential for this setup?
A) Write a custom script to check for project changes and handle deployment.
B) Configuring dbt Cloud to trigger documentation deploys.
C) Implementing a CI/CD pipeline that includes dbt build steps.
D) Enabling webhooks in your Git repository provider.
Solutions:
| Question # 1 Answer: A,C | Question # 2 Answer: B,C | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: A,C,D |
