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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing Data Pipelines	15%	- Deployment strategies - CI/CD integration - Pipeline orchestration
Topic 2: Implementing dbt Tests	10%	- Custom tests - Test configuration and execution - Built-in tests
Topic 3: Developing dbt Models	20%	- Model optimization 1. Model contracts 2. Performance tuning - Model design and structure 1. Materialization types 2. Ref and source functions 3. Incremental models
Topic 4: Debugging and Error Resolution	15%	- Debugging techniques - Resolving data quality issues - Identifying modeling errors
Topic 5: Leveraging dbt State	5%	- State-aware operations - State management
Topic 6: Creating and Maintaining Documentation	10%	- Descriptions and metadata - Documentation standards - Generating documentation
Topic 7: dbt Models Governance	15%	- Naming conventions and standards - Project organization and structure - Version control integration
Topic 8: External Dependencies	10%	- External sources integration - Managing snapshots - Using packages

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q233-Q238):

NEW QUESTION # 233

Answer:

Explanation:

Explanation:
Drops and recreates a definition of a query which is executed at the time it's referenced
# view
Inserts or updates records of an existing object in the warehouse using a delta
# incremental
Code belonging to the model is inserted into the places the model is referenced
# ephemeral
Drops and recreates an object in the warehouse as stored results when using dbt run
# table

NEW QUESTION # 234
You're reviewing a pull request and notice a subtle logic error that wasn't caught in automated tests. How do you best approach this within the code review?

A. Reject the pull request outright and ask the author to fix the error and resubmit.
B. Leave a comment explaining the error and provide a suggestion for how to fix it.
C. Simply fix the error directly in the pull request without leaving a comment.
D. Create a separate issue in the issue tracker, detail the bug, and reference it in the pull request.

Answer: B

Explanation:
Providing constructive feedback guides the PR author toward a solution, facilitating improvement and knowledge sharing.

NEW QUESTION # 235
(Multiple Select)

A. materialized: view
B. database
C. model-paths
D. test-paths

Answer: C,D

Explanation:
model-paths and test-paths specify project directory structures- Options C would be within a model configuration, and D could be in a profile.

NEW QUESTION # 236
You have a complex dbt model that is essential for production reporting. To minimize the risk of breaking this model during development, what strategy might you employ?

A. Rely on your data warehouse's native rollback functionality to reverse changes if needed.
B. Configure production jobs to use a pinned version of the dbt model.
C. Create a duplicate model in production and only sync changes once they're fully tested-
D. Write extensive tests targeting this model in your production environment

Answer: B,C

Explanation:
A provides stability for the critical part of production- C offers a staging-like area within production. B is good, but doesn't prevent the breakage upstream. D isnt generally how dbt models operate-

NEW QUESTION # 237
You receive a compilation error stating:Model depends on a node named 'ref data' which was not found

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

Answer: D

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

NEW QUESTION # 238
......

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