dbt Labs dbt-Analytics-Engineering最新対策問題 & dbt-Analytics-Engineering模擬試験サンプル

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> dbt Labs dbt-Analytics-Engineering最新対策問題 <<

dbt-Analytics-Engineering模擬試験サンプル、dbt-Analytics-Engineering認証資格

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q303-Q308):

質問 # 303
(Multiple Select)

A. Catching subtle errors or discrepancies that may not cause immediate functional issues.
B. Ensuring data synchronization after a migration.
C. Testing performance under a simulated heavy load.
D. Validating changes during development before deploying a new dbt model version.

正解：A、B、D

解説：
These tests help ensure alignment between the source of truth and the analytics environment. D (performance testing) is a different type of test.

質問 # 304
13. An analyst on your team has informed you that the business logic creating the is_active column of your stg_users model is incorrect.
You update the column logic to:
case
when state = 'Active'
then true
else false
end as is_active
Which test can you add on the state column to support your expectations of the source data? Choose 1 option.

A. - name: is_active
tests:
- not_null
- unique
B. - name: state
tests:
- not_null
- unique
C. - name: is_active
tests:
- accepted_values:
values: ['active', 'churned', 'trial']
- not_null
D. - name: state
tests:
- accepted_values:
values: ['active', 'churned', 'trial']
- not_null

正解：D

解説：
The purpose of this question is to determine how to validate that the input values in the state column support the business logic that determines the is_active field. Since the logic checks whether state = 'Active', it is critical that the state column only contains values that the business process expects. In the example shown, acceptable states appear to be: 'active', 'churned', and 'trial'.
The correct way to enforce this expectation is to apply an accepted_values test on the state column. This ensures that any unexpected state (e.g., 'inactive', 'pending', 'deleted', or NULL) will cause the test to fail, alerting the team that the upstream system is producing unexpected or invalid values. Additionally, adding not_null ensures every user record contains a valid state.
Option A is the only configuration that applies the accepted values test to the correct column (state) and reflects the expected domain of values.
Options B and D incorrectly apply tests to the derived column is_active, not the source column that needs validation. Option C only checks nullability and uniqueness, which does not validate the range of allowed values and thus does not protect the business logic.
Therefore, Option A is the only correct answer.

質問 # 305
You've created a pull request, and a discussion in the code review reveals issues with the approach you used in your feature branch. Rather than continuously updating the original branch, what's a strategy to address the feedback while keeping a clean pull request history?

A. Directly modify the commits on your existing feature branch using git rebase -i.
B. Close the original pull request and create a new branch with the revised implementation.
C. Request that your collaborators edit the code directly within the pull request interface.
D. Add the changes as a new commit to your feature branch, explaining the revisions in the pull request comments.

正解：B

解説：
A: A new branch often provides the cleanest history for the reworked feature. B: Altering existing commits should be used judiciously, as it 'rewrites' history and can cause problems for collaborators.

質問 # 306
A new feature you're developing requires significant changes across multiple models in your dbt project. Which branching strategy would be the most suitable and why?

A. Create a feature branch for the new development to isolate your work.
B. Avoid using branches altogether, as they can introduce unnecessary complexity into a dbt workflow
C. Create separate branches for each model you'll be modifying to minimize conflicts.
D. Work directly on the main branch, as it ensures your changes are always up-to-date.

正解：A

解説：
Feature branches provide a dedicated workspace allowing you to work on changes without impacting the stability of the main branch. This is vital for larger changesets.

質問 # 307
Consider this DAG:
app_data.detail_categories -> stg_detail_categories -> skills_with_details app_data.details -> stg_details -> lessons_with_details What will support making this DAG more modular? Choose 1 option.

A. Consolidate the two staging models into one model and then use this downstream for both skills_with_details and curriculum_with_details.
B. Join stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
C. Union stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
D. Combine lessons_with_details and skills_with_details into one wide table called curriculum_with_details.
E. Examine the SQL of lessons_with_details and skills_with_details to see if there is a candidate for an intermediate model.

正解：E

解説：
Modularity in dbt means breaking transformations into small, reusable steps that are easy to reason about, test, and reuse. In this DAG, two staging models (stg_detail_categories and stg_details) feed into two mart- level models (skills_with_details and lessons_with_details). If there is repeated logic in those two mart models-such as common joins, filters, or derived fields-that logic is a good candidate to be extracted into an intermediate model.
Option E follows dbt best practices: inspect the SQL of lessons_with_details and skills_with_details and identify shared logic to move into a new intermediate model (for example, int_details_with_categories). Both downstream models can then reference this intermediate model using ref(), centralizing logic and improving maintainability.
Options A and B push joins/unions into the staging layer, which should focus on clean, 1:1 representations of sources-not combining multiple sources. Option C collapses distinct staging models into one, reducing clarity and breaking the source-to-staging contract. Option D creates an overly wide, less reusable final table instead of focusing on shared reusable logic.
Therefore, the action that truly increases modularity is Option E: creating an intermediate model based on common SQL patterns.

質問 # 308
......

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