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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q267-Q272):

NEW QUESTION # 267
(Multiple Select)

A. IJse the -select flag with dbt run to execute models incrementally, starting with the most upstream dependency.
B. Run dbt docs generate and dbt docs serve to inspect the lineage graph.
C. Write custom macros to log intermediate data for each model.
D. Add -full-refresh flag to dbt run to ensure all models are rebuilt from scratch.

Answer: A,B

Explanation:
Visualization and incremental execution aid debugging. Custom logging might be necessary but is more time-consuming to implement. A full refresh isn't targeted for debugging.

NEW QUESTION # 268
During a code review, a collaborator suggests modifying a small portion of code you had already committed to your feature branch. What's the most efficient way to address this without creating a completely new commit?

A. Use git revert to undo your previous commit, make the change, and then re-commit.
B. Use git commit -amend to modify your most recent commit.
C. Create a new branch extending from your feature branch, make the change, and open a separate pull request.
D. Explain to your collaborator that modifying committed code is not recommended, and the change should wait for a future update.

Answer: B

Explanation:
Git commit -amend allows seamless modification of your most recent commit, preserving a clean commit history.

NEW QUESTION # 269
13. An analyst on your team has informed you that the business logic creating the is_active column of your stg_users model is incorrect.
You update the column logic to:
case
when state = 'Active'
then true
else false
end as is_active
Which test can you add on the state column to support your expectations of the source data? Choose 1 option.

A. - name: is_active
tests:
- accepted_values:
values: ['active', 'churned', 'trial']
- not_null
B. - name: is_active
tests:
- not_null
- unique
C. - name: state
tests:
- accepted_values:
values: ['active', 'churned', 'trial']
- not_null
D. - name: state
tests:
- not_null
- unique

Answer: C

Explanation:
The purpose of this question is to determine how to validate that the input values in the state column support the business logic that determines the is_active field. Since the logic checks whether state = 'Active', it is critical that the state column only contains values that the business process expects. In the example shown, acceptable states appear to be: 'active', 'churned', and 'trial'.
The correct way to enforce this expectation is to apply an accepted_values test on the state column. This ensures that any unexpected state (e.g., 'inactive', 'pending', 'deleted', or NULL) will cause the test to fail, alerting the team that the upstream system is producing unexpected or invalid values. Additionally, adding not_null ensures every user record contains a valid state.
Option A is the only configuration that applies the accepted values test to the correct column (state) and reflects the expected domain of values.
Options B and D incorrectly apply tests to the derived column is_active, not the source column that needs validation. Option C only checks nullability and uniqueness, which does not validate the range of allowed values and thus does not protect the business logic.
Therefore, Option A is the only correct answer.

NEW QUESTION # 270
You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?

A. rely on native version history features in your data warehouse (if available).
B. Write a dbt macro to create a temporary table with historical data to compare against.
C. Use a custom 'accepted_values' test together with a dbt seed.
D. Use a snapshot to preserve a point-in-time reference of the column.

Answer: A,B,D

Explanation:
A provides a clean comparison point C enables more complex historical analysis. D leverages built-in warehouse features, if you have them.

NEW QUESTION # 271
You're refactoring a complex SQL query into a dbt model. You notice that the model's output has significantly fewer rows than expected. Which of the following are potential causes?

A. An incorrect materialization setting on the model.
B. There may be a LIMIT statement leftover from development.
C. Upstream data has quality issues with null values or unexpected joins.
D. Your model has accidental WHERE conditions that are too restrictive.

Answer: B,C,D

Explanation:
All except D could limit the result set more than expected. Materialization settings impact how tables are built, not the SQL logic itself.

NEW QUESTION # 272
......

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