dbt-Analytics-Engineering合格内容、dbt-Analytics-Engineering認定資格

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt-Analytics-Engineering認定資格 & dbt-Analytics-Engineering復習範囲

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q131-Q136):

質問 # 131
You are working on a complex dbt model with many Common Table Expressions (CTEs) and decide to move some of those CTEs into their own model to make your code more modular.
Is this a benefit of this approach?
The new model can be documented to explain its purpose and the logic it contains.

A. Yes
B. No

正解：A

解説：
Yes, this is a benefit of breaking large CTE-heavy SQL models into modular dbt models. According to dbt and Analytics Engineering best practices, modularity improves clarity, maintainability, and documentation quality. When CTEs remain embedded inside a single large SQL file, their purposes are often unclear, difficult to document, and hard for other developers to reuse. By extracting a logical CTE into its own model, dbt treats it as a first-class resource-meaning it can have its own description, tests, documentation, lineage, and metadata defined in YAML.
dbt's documentation system allows each model to include a description explaining what the transformation does, the assumptions being made, and the expected behavior of the data. This aligns with the Analytics Engineering principle of creating self-documenting pipelines, where transformations are transparent and easier for downstream users to understand.
Additionally, modular models improve lineage visualization in the DAG. Instead of a single model hiding multiple transformation layers, a modular structure reveals how data flows through each intermediate step, helping both debugging and governance. Modularization also enables reusability-other models can reference the intermediate model rather than rebuilding the same logic through duplicated CTEs, supporting DRY (Don't Repeat Yourself) principles.
Therefore, moving CTEs into separate dbt models absolutely provides a documentation benefit and improves the overall engineering quality of the project.

質問 # 132
(Multiple Select)

A. Downstream models relying on the staging model will continue to function.
B. The staging model run will fail.
C. The dbt run will succeed, but the staging model will have null values due to the missing data.
D. dbt will issue a warning but continue processing.

正解：B、C

解説：
A and C are correct. A missing dependency will cause SQL errors, and nulls would likely occur where the dropped table's data was used. B is incorrect. Downstream models will often fail if an upstream model fails. D is incorrect. dbt's strict dependency handling is likely to stop the process and throw errors.

質問 # 133
You're working in a highly collaborative environment. How could you manage contributions and updates to complex lineage macros to ensure maintainability and prevent conflicts?

A. Limit the number of people authorized to make changes to sensitive lineage macros-
B. Establish coding style guides and peer-review processes specifically for macros.
C. All of the above-
D. Utilize a robust version control system (e.g., Git) for macro code.

正解：C

解説：
A Version control is essential for tracking changes and collaboratiom B: Style guides and reviews improve code quality and reduce errors. C: Controlled access can be important, while still allowing for collaboration through review processes.

質問 # 134
(Multiple Select)

A. Continuous Integration (Cl) tools.
B. Tools specializing in schema change detection.
C. Data integration testing frameworks (e.g., Great Expectations)
D. Anomaly detection platforms.

正解：B、C、D

解説：
All of these address different aspects of advanced data validation that complement the basic tests within dbt. Cl tools (D) are involved in running these checks but are not the data validation tools themselves.

質問 # 135
The results of a dbt model contain values outside the expected range. You suspect an issue with Jinja templating. Where might this issue originate?

A. All of the above.
B. Within a dbt_projectyml model or source configuration.
C. In the model's SQL, where you're using Jinja for calculations or dynamic logic.
D. In a macro used by the model.

正解：A

解説：
Jinja templating can exist in your project configuration files, macros, and the model SQL itself.

質問 # 136
......

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