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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Leveraging dbt State	5%	- State management - State-aware operations
Creating and Maintaining Documentation	10%	- Generating documentation - Documentation standards - Descriptions and metadata
External Dependencies	10%	- External sources integration - Using packages - Managing snapshots
dbt Models Governance	15%	- Version control integration - Naming conventions and standards - Project organization and structure
Developing dbt Models	20%	- Model optimization 1. Model contracts 2. Performance tuning - Model design and structure 1. Ref and source functions 2. Incremental models 3. Materialization types
Managing Data Pipelines	15%	- Deployment strategies - CI/CD integration - Pipeline orchestration
Implementing dbt Tests	10%	- Test configuration and execution - Custom tests - Built-in tests
Debugging and Error Resolution	15%	- Debugging techniques - Identifying modeling errors - Resolving data quality issues

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
While working on a feature branch, you need to incorporate some critical bug fixes that were merged into the main branch. Which Git commands, or combination of commands, would you use?

A. git merge main
B. git checkout main followed by git pull
C. git rebase main
D. Both B and C are viable options.

Answer: A,C

Explanation:
B: Rebasing integrates the main branch's changes onto your feature branch, keeping a linear history. C: Merging directly creates a merge commit, making the history slightly less linear but often easier to track when features are integrated.

NEW QUESTION # 247
Match the macro to the appropriate hook so that the correct execution steps comply with these rules:
* macro_1() needs to be executed after every dbt run.
* macro_2() needs to be executed after a model runs.
* macro_3() needs to execute before every dbt run.
* macro_4() needs to be executed before a model runs.

Answer:

Explanation:

Explanation:
Hook 1
on-run-end: "{{ macro_x() }}"
The Answer:
macro_1
Hook 2
models:
<my_dbt_project>:
post-hook: "{{ macro_x() }}"
The Answer:
macro_2
Hook 3
on-run-start: "{{ macro_x() }}"
The Answer:
macro_3
Hook 4
{{
config(
pre-hook: "{{ macro_x() }}"
)
}}
macro_4
dbt supports run-level hooks and model-level hooks.
Run-level hooks fire once per invocation, while model- level hooks fire around each individual model.
on-run-end is a run-level after hook that executes once after the entire dbt command completes.
Because macro_1() must run after every dbt run, it correctly belongs here.
The post-hook configured under the models: section runs after each model in that scope finishes building.
This matches the requirement for macro_2() to execute after a model runs.
on-run-start is a run-level before hook and fires once before dbt begins executing any models for that command, making it the right place for macro_3() which must run before every dbt run.
Finally, the pre-hook specified inside a model's config() block runs before that specific model is built.
Since macro_4() must execute before a model runs, it belongs in the pre-hook configuration.
Thus the correct mapping is:
* on-run-end # macro_1
* model post-hook # macro_2
* on-run-start # macro_3
* model pre-hook # macro_4.

NEW QUESTION # 248
In what situations might it be acceptable to use raw data directly for reporting or analytics purposes?

A. When performance is a top priority, and the overhead of additional data transformations is undesirable.
B. Generally, using raw data directly for reporting should be avoided due to potential quality and consistency issues.
C. When dealing with simple analyses, and there's high confidence in the quality of the raw data source.
D. In environments where strict data governance and transformation processes are in place.

Answer: B

Explanation:
Raw data is often unpredictable- However, under specific conditions like those listed in A, B, or C, it could be considered if the risks and trade-offs are well understood.

NEW QUESTION # 249
(Multiple Select)

A. Your development profile in dbt_projectyml has incorrect connection details.
B. You have accidentally granted write permissions to the production data source during testing.
C. Environment variables used for sensitive credentials are not set correctly in production.
D. Your production database has stricter data type constraints than development.

Answer: C,D

Explanation:
Explanation: A could reveal issues only present when interacting with the real production data. C is a common source of environment-specific errors. B would likely cause errors everywhere. D is a serious issue, but wouldn't cause a regular dbt run to fail differently.

NEW QUESTION # 250
You've updated a model's description in a YAML file to reflect a new business requirement. You've also added additional context to the column descriptions. After regenerating the documentation, you notice the model description has updated, but the column descriptions have not. What could be the most likely reasons?

A. You are viewing an older cached version of the documentatiom
B. The model's YAML file has incorrect syntax preventing column descriptions from rendering.
C. The documentation was not configured correctly to display column descriptions.
D. You forgot to run dbt docs generate after modifying the YAML file.

Answer: A,D

Explanation:
Explanation: B:
The dbt docs generate command is essential for updating the documentation website with changes made in the project files. D: Web browsers often cache content; you might need to clear your cache or force a refresh to see the latest changes.

NEW QUESTION # 251
......

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