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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q135-Q140):

NEW QUESTION # 135
(Multiple Select)

A. You made a typo when defining the column name in your model.
B. A ref function referencing another model is incorrect.
C. Your dbt tests are invalid.
D. An upstream source table schema was updated without updating the dbt model.

Answer: A,B,D

Explanation:
All but option D could lead to this specific error. Tests wouldn't directly cause a column mismatch, but they might help you catch the error.

NEW QUESTION # 136
You're refactoring a dbt project with circular dependencies (e.g., Model A references Model B, which references Model A).
Select the most appropriate strategy:

A. Add the -full-refresh flag to dbt run to force execution.
B. Rewrite SQL logic to remove the circular references.
C. Introduce a redundant intermediate model to resolve the circularity.
D. Create snapshots to break the dependency chain.

Answer: B

Explanation:
Circular dependencies are a fundamental design flaw. 'Snapshots' and -full-refresh are workarounds. Introducing redundant models creates maintenance overhead.

NEW QUESTION # 137
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. Your model has accidental WHERE conditions that are too restrictive.
D. Upstream data has quality issues with null values or unexpected joins.

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 # 138
You're working in a highly regulated environment. Merges into the main branch require multiple approvals. Which additional steps might need to be incorporated into your merge process?

A. Link pull requests to detailed change documentation and impact analysis.
B. All of the above
C. Use a protected branch workflow on the remote repository with required reviews.
D. Utilize signed commits to add a layer of traceability and verification-

Answer: B

Explanation:
A: Clear documentation is essential for compliance and audit trails. B: Protected branches enforce the approval process at the repository level. C: Signed commits offer proof of authorship and agreement on the changes-

NEW QUESTION # 139
You receive a compilation error stating:Model depends on a node named 'ref data' which was not found

A. Search for a model named ref_data within your project or any included packages.
B. Review downstream models that might be referencing ref_data incorrectly.
C. Check the configuration of the ref_data data source.
D. Examine the SQL within the model where the error occurs for typos or incorrect ref() usage.

Answer: A

Explanation:
Compilation errors often mean missing dependencies. Start by ensuring ref_data actually exists as a model.

NEW QUESTION # 140
......

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