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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing dbt Models | 20% | - Model design and structure
|
| Managing Data Pipelines | 15% | - Deployment strategies - CI/CD integration - Pipeline orchestration |
| External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| Creating and Maintaining Documentation | 10% | - Generating documentation - Descriptions and metadata - Documentation standards |
| Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors |
| Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution |
| Leveraging dbt State | 5% | - State management - State-aware operations |
dbt Labs dbt Analytics Engineering Certification Sample Questions:
You're collaborating with a third-party analytics vendor who requires access to a subset of processed data within your dbt project for integration into their platform. How might you approach this securely?
- A. Generate a dedicated readout schema in your data warehouse with appropriate grants for the vendor's user
- B. Set up a data sharing mechanism through your data warehouse, if it supports this functionality, enabling controlled access.
- C. Utilize a dbt package to export specific models to CSV or JSON format and provide those files to the vendor
- D. A combination of the above might be needed, depending on the vendor's integration capabilities-
Correct Answer: D 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
You've merged a pull request but later discover it introduced a regression (unexpectedly broke existing functionality). What's the most common initial step to address this?
- A. Run git revert on the merge commit to undo the changes.
- B. Use git blame or git log to pinpoint the commit that caused the issue.
- C. Coordinate with the original author of the merged pull request.
- D. Open a new pull request with a fix for the regression.
Correct Answer: B,C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
You have a dbt project with models defined as views. After deploying to a production database, the view objects are present, but no data is displayed when querying them. What might be the root cause?
- A. There's a bug in dbt's SQL generation that specifically affects views in certain environments.
- B. Your dbt job didn't run successfully, so the views were created empty.
- C. Permissions granted in production don't allow reading data from the underlying tables.
- D. Dbt view materializations behave differently in production.
Correct Answer: C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
A performance-critical model depends on multiple complex transformations that would benefit from being pre-computed. Direct querying of this model should be as fast as possible. Which materialization is likely the best choice?
- A. Table
- B. Ephemeral
- C. Incremental
- D. View
Correct Answer: A 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
You've created a macro to display upstream dependencies differently from downstream dependencies in the DAG. However, after re-generating the documentation, the visualization doesn't seem to have changed. What should you investigate?
- A. The built-in DAG functionality might have limitations preventing this type of customization.
- B. Ensure the macro is named correctly and located in the appropriate directory (macros folder) of your dbt project.
- C. Verify that your web browser supports the CSS and JavaScript used for custom DAG styling.
- D. Update the cache expiration settings of your web browser.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
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