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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

dbt Labs dbt Analytics Engineering Certification Sample Questions:

1. A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:

A) All of the above.
B) Resubmit the pull request as several smaller, more focused pull requests.
C) Provide a comprehensive summary of the intent and scope of the changes within the pull request description.
D) Use detailed commit messages to explain the rationale behind each set of related changes.

2. While onboarding a new team member, you need to ensure consistency between their local development setup and your existing production dbt deployment. Which of these are essential?

A) All of the above.
B) Providing clear documentation on configuration of profiles_yml to connect to the correct targets.
C) Setting up access and permissions in the data warehouse for the new user, aligning with your team's security practices.
D) Enforcing version control and utilizing a requirements txt file to pin dbt and adapter dependencies.

3. You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A) Run git rebase main followed by git commit -amend.
B) git cherry-pick specific commits from your branch and reapply them.
C) Use git rebase -i (interactive rebase) to squash commits together.
D) Manually copy your files into a new branch, commit, and issue a pull request.

4. A team member has opened a pull request, but conflicts arise when you attempt to merge it. Which of the following are essential steps in the conflict resolution process?

A) All of the above.
B) Communicate with your team member to understand the intent behind the conflicting changes.
C) Manually edit the affected files in your favorite text editor to resolve the differences.
D) Utilize Git's built-in merge conflict markers to identify areas of disagreement

5. A colleague asks, "Why go through the trouble of using dbt? Can't we just write SQL scripts and schedule them?" Which of the following is the most compelling argument for using dbt?

A) dbt models are typically more performant than hand-written SQL scripts.
B) dbt promotes modularity, reusable code, and testing within data transformation pipelines.
C) dbt offers native support for advanced scheduling and orchestration features.
D) dbt automatically generates user-friendly data lineage visualizations.

Solutions: