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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Models	25%	- Sources and references - Seeds - Writing and managing SQL models - Snapshots - Materializations (table, view, ephemeral, incremental)
Topic 2: Deployment and Orchestration	15%	- Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - Git version control integration - CI/CD with dbt Cloud
Topic 3: Data Transformation Techniques	25%	- Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models
Topic 4: Testing and Documentation	20%	- dbt docs and DAG visualization - Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - Custom data tests
Topic 5: dbt Fundamentals	15%	- dbt project structure - dbt workflow and best practices - dbt Core vs dbt Cloud

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
You're merging changes from a feature branch into your main branch. Which of the following Git commands or strategies help keep a clean and easy-to-follow project history?

A. Utilizing git rebase main before merging to linearize the history.
B. Employing git squash merge for condensing feature branch commits.
C. Using git merge to create a merge commit.
D. All of the above can be valid strategies, each with different trade-offs.

Answer: D

Explanation:
A Merge commits preserve branch structure but can make history more complex. B: Rebasing creates a linear history, making it simpler to follow C: Squash merges combine multiple commits, simplifying the history of major features. The best approach depends on your team's preferences and the feature's complexity

NEW QUESTION # 72
Your dbt project pulls data from a SaaS application using a third-party adapter. You observe performance issues. Where might you start optimizing?

A. Check your dbt_project.yml for project-level performance settings.
B. Review the source definitions to see if unnecessary data is being selected.
C. Investigate the third-party adapter's settings or documentation for potential bottlenecks.
D. Configure the source as an incremental materialization within your project file.

Answer: C

Explanation:
Third-party adapters often introduce bottlenecks. Start with optimizing whats specific to the adapter The others might help in some cases but are less directly dependent on the external data source.

NEW QUESTION # 73
Dependency Break

A. Setting up alerts or notifications if dbt runs fail unexpectedly in a production environment.
B. Using the dbt deps command to visualize dependencies and anticipate potential impacts.
C. Adding schema tests to the source table to catch structural changes early on.
D. All of the above.

Answer: D

Explanation:
Proactive schema tests, dependency awareness, and failure monitoring enhance resilience.

NEW QUESTION # 74
A model passes its tests during development, but later fails when deployed on a less powerful production database. Which of the following might be the reason for this discrepancy?

A. Performance-related test thresholds are too lenient in development.
B. The test was accidentally disabled in the production dbt project.
C. There's a bug in the version of dbt that runs in production.
D. Source data distributions differ significantly between environments.

Answer: A,D

Explanation:
A and B could lead to tests passing in a forgiving environment but not catching issues in a more resource- constrained one. C would likely be caught earlier, and D is less likely

NEW QUESTION # 75
You've introduced a new series of dbt models and want to run them in isolation to verify results before integrating them into the main build process. Which dbt command options and flags are likely to be useful?

A. dbt run -m +model_path to explicitly select a model by its file path.
B. dbt test --select source name to validate the new models' source data.
C. dbt build -exclude model_name to temporarily skip certain models.
D. dbt run -s to target models with a specific tag

Answer: A,D

Explanation:
A and C provide precise selection mechanisms. B is for exclusion, not focused execution- D is about tests, not model runs-

NEW QUESTION # 76
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