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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q318-Q323):

NEW QUESTION # 318
A colleague has introduced a new sensitive column into a source table. You need to update the dbt project documentation accordingly. Which of the following actions should you take?

A. Consult your organization's data governance policies and consider additional measures beyond simple documentation.
B. Add the column description and tag it using a custom sensitive tag in the YAML file.
C. Add a clear description of the sensitive column in its corresponding YAML file.
D. Update the source documentation and add references to that documentation in the appropriate model's YAML file

Answer: A,B,D

Explanation:
B). Tagging sensitive data helps categorize it, allowing for potential filtering or access control within the documentation. C: Handling sensitive data goes beyond simple documentation. Its crucial to follow your organization's data security and governance protocols. D: Referencing source documentation provides full context and traceability of the sensitive data's origin.

NEW QUESTION # 319
You need to regularly run performance tests to benchmark a time-sensitive incremental model. These tests involve more complex setup than standard dbt tests. How could you efficiently integrate them into your workflow?

A. Write the tests as generic tests and use dbt's built-in warning thresholds.
B. Define the performance tests as their own dbt project, triggered separately-
C. Convert them into dbt seeds to easily execute them alongside model runs-
D. Create a dbt operation (macro) to encapsulate the test logic and invoke it as needed.

Answer: B,D

Explanation:
A provides separation and specialized configuration if needed. C offers reusability and the ability to run from within a dbt workflow. B (seeds) isnt suitable for complex testing logic. D (generic tests) lack the power required for performance benchmarking.

NEW QUESTION # 320
You've written dbt tests that assume a certain distribution of values in a column. Later, the upstream process generating the data changes unexpectedly. What's the most likely consequence?

A. dbt will automatically adjust your tests to accommodate the new distribution.
B. The tests will fail, preventing the changed data from being loaded, preserving the old behavior.
C. Your dbt models may silently produce incorrect results due to the changed distribution.
D. The changed data distribution has no impact on downstream dbt models.

Answer: C

Explanation:
This highlights the risk of outdated assumptions. Without failing tests, you may not even realize your analysis is now based on flawed data.

NEW QUESTION # 321
You define a source table and include an override:YAML

A. The overrides property needs to be defined at the source level, not the table level.
B. The value 'my_project' is not a recognized keyword by dbt.
C. The overrides property is not valid for tables within your dbt_project.yml.
D. You need to create a macro named 'my_project' that contains the specific overriding logic.

Answer: A

Explanation:
While dbt supports overrides, they generally work at higher levels within your project file, not at the model level.

NEW QUESTION # 322
A common dimension table is used in 80% of your models. What might be a side effect of frequently changing the structure of this dimension?

A. It would cause errors in models that are not directly dependent on this dimension.
B. You might need to update a large number of downstream models.
C. Compile times would significantly increase across the project.
D. The dbt docs output would become unmanageable.

Answer: B

Explanation:
Changing core dimensions has ripple effects on dependent models. While docs could become more complex, the primary issue is maintaining those dependencies.

NEW QUESTION # 323
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