dbt-Analytics-Engineering的中問題集 & dbt-Analytics-Engineering日本語版と英語版

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q173-Q178):

質問 # 173
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?

A. Manually trace the dependencies in your model files.
B. Visually inspect the manifest.json file.
C. Run dbt docs generate and inspect the generated documentation.
D. Use dbt Is -select

正解：C

解説：
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.

質問 # 174
You accidentally pushed a large file containing sensitive data to a public remote repository. What immediate actions should you take?

A. Use git reset -hard to revert the commit and remove the file locally.
B. Both B and C.
C. Permanently remove the sensitive file from Git history using tools like git filter-branch.
D. Contact the repository hosting service for assistance.

正解：C、D

解説：
B: You must ensure the sensitive data is completely removed from the history of the repository. C: The hosting service might have additional security measures or be able to assist in preventing the sensitive data from being accessed

質問 # 175
(Multiple Select)

A. Populating a lookup table with country codes.
B. Inserting large volumes of historical transactional data for backfilling purposes.
C. Defining generic tests that apply to multiple data sources.
D. Loading configuration data for production environments that differs from development settings.

正解：A、D

解説：
seeds are ideal for static reference data and environment-specific configurations. Large data backfills are usually handled outside of 'seeds'. Generic tests would be defined in the tests section.

質問 # 176

正解：

解説：

Explanation:
Update int_order_items by replacing:
* {{ source('tpch', 'orders') }} # {{ ref('stg_tpch_orders') }}
* {{ source('tpch', 'lineitem') }} # {{ ref('stg_tpch_line_items') }}
In dbt's recommended modeling pattern, sources should be referenced only in the staging layer. Downstream models (intermediate and mart/fact models) should reference staging models via ref(), not the raw source() directly. This keeps all raw-to-clean logic centralized in the staging layer and makes refactoring and documentation easier.
In the original DAG, int_order_items depends directly on tpch.orders and tpch.lineitem via source(), and staging models also depend on those same sources. That creates parallel paths from the sources and breaks the clean layered architecture.
By updating the int_order_items SQL to use:
from {{ ref('stg_tpch_orders') }}
join {{ ref('stg_tpch_line_items') }} ...
instead of:
from {{ source('tpch', 'orders') }}
join {{ source('tpch', 'lineitem') }} ...
you ensure that int_order_items sits entirely downstream of the staging layer. The new DAG becomes linear: sources # staging # intermediate (int_order_items) # any further marts. This improves modularity, reusability of cleaned logic, and makes the DAG easier to reason about and maintain.

質問 # 177
Which of the following is NOT a direct benefit of thorough source, table, and column descriptions in dbt?

A. Better data quality due to the automated enforcement of column descriptions
B. Improved lineage and data traceability within the documentation.
C. Increased discoverability for other team members working on the project
D. Faster dbt model compilation and execution times.

正解：D

解説：
Explanation: While good descriptions have numerous benefits, they don't inherently speed up model compilation and execution within dbt-

質問 # 178
......

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