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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

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dbt-Analytics-Engineering試験関連赤本 & dbt-Analytics-Engineering合格対策

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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
(Multiple Select)

A. Pre-calculate frequently used periods (e.g., current month, previous month) and store them as intermediate CTEs.
B. Introduce redundant columns in source tables to store pre-calculated percentage changes.
C. Ensure relevant columns in underlying tables have appropriate indexes.
D. Utilize window functions to efficiently compute the difference without self-joins.

正解：C、D

解説：
Window functions excel at period-over-period calculations. Indexes are crucial for accelerating queries. Options A and B could help but introduce potential maintenance and storage tradeoffs.

質問 # 132
Select the dbt materialization that typically DOES NOT persist its results to disk:

A. Ephemeral
B. Table
C. Incremental
D. Snapshot

正解：A

解説：
Ephemeral materializations are computed within CTEs (Common Table Expressions). Their results exist only during the query's execution and are useful for intermediate steps within complex transformations.

質問 # 133
A team member proposes directly joining multiple raw source tables into a single large model for convenience.
Which of the following is a potential long-term consequence of this approach?

A. Your DAG might inadvertently develop circular dependencies.
B. Incremental materialization strategies would no longer be applicable.
C. The dbt test command might run slower due to the complex join logic.
D. Maintenance complexity increases as business logic or source tables change.

正解：D

解説：
Directly joining raw sources sacrifices modularity and flexibility. While option A is partially true, B highlights a more significant maintainability risk.

質問 # 134
You've just added some schema tests to a critical model. Which command should you execute to ensure these new tests are run?

A. dbt test
B. dbt seed
C. dbt run
D. dbt compile

正解：A

解説：
The dbt test command is specifically designed to execute the tests you've defined within your models.

質問 # 135
You are working with git to version control the dbt logic.
Order these steps to add or modify transformations to your dbt project.

正解：

解説：

* Create a new branch in git and switch to itB. Update the dbt code according to the new logic
C. Create a Pull / Merge RequestD. Run some automated CI checks and/or manual review of the updated codeE. Merge the updated code to the main git branch
* Version control best practices in dbt follow the same engineering workflow used in modern software development. The first step is always to create a new git branch and switch into it, ensuring all work is isolated from production-ready code and allowing your team to develop safely without affecting others.
Once inside the feature branch, you update the dbt code according to the new logic, which may include modifying models, tests, macros, or documentation.
* Next, you create a Pull Request (PR), also known as a Merge Request, to propose integrating your changes into the main branch. This is important because dbt projects are collaborative, and PRs facilitate peer review, enforce project standards, and prevent regressions. Once the PR is created, automated CI pipelines-such as running dbt build, schema tests, data quality checks, and code-style checks-are executed. Reviewers may also manually inspect code for logic correctness, naming conventions, and modeling consistency.
* After all checks have passed and reviewers approve the PR, the final step is to merge the updated code into the main branch, making the new transformations part of the production dbt project. This workflow ensures governance, reliability, and auditable development practices, all of which are core principles in analytics engineering.

質問 # 136
......

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