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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

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

>> dbt-Analytics-Engineering資格練習 <<

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q136-Q141):

質問 # 136
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. In a macro used by the model.
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. All of the above.

正解：D

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

質問 # 137
After merging a large pull request, you notice a critical issue introduced in one of the commits. What actions might be necessary?

A. Identify the problematic commit using git log or git blame.
B. Use git revert to create a new commit that specifically undoes the problematic changes.
C. All of the above could be needed as part of the fix.
D. Use git cherry-pick to apply specific commits from the reverted change to another branch, effectively restoring the necessary parts.

正解：C

解説：
A: git revert provides a way to 'undo' changes safely. B: Identifying the source of the issue is essential. C: git cherry-pick lets you selectively reintroduce only the necessary portions in a controlled manner.

質問 # 138
You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?

A. Configure a dbt hook to run before the job, dynamically checking freshness of the source table.
B. Redesign the model with an incremental materialization strategy that gracefully handles partial updates.
C. Set the -full-refresh flag for the dbt job to ensure all tables are materialized.
D. Add a depends_on relationship pointing to the potentially late table within your model.

正解：A、B

解説：
C provides a proactive check directly before execution. D makes the model more resilient to source data timing issues. A wouldnt prevent the job from running unless you modify its execution setup. B is overkill and inefficient.

質問 # 139
(Multiple Select)

A. Utilize a YAML validator tool independently of dbt to check for general formatting errors.
B. If available, compare your schema.yml to a previous working version to spot problematic changes.
C. Refer to the dbt documentation for a comprehensive list of valid properties within schema files.
D. Introduce a global persist_docs setting within your dbt_project.yml file to resolve schema inconsistencies.

正解：A、C

解説：
Understanding valid schema properties and general YAML linting are key. Version comparison can be useful, while persist_docs is less relevant to initial compilation errors.

質問 # 140
You need a model to track weekly cohort retention, with the ability to slice data across multiple dimensions. The underlying data is voluminous. Which approach lays a good foundation for scalability?

A. Normalize the cohort data and dimensions, using joins for analysis.
B. Introduce a JSON column to store dimensions dynamically, reducing the number of columns.
C. A wide table with columns for each potential filtering combination (country, product category, acquisition channel, etc.).
D. Pre-calculate all possible cohort and dimension combinations as an incremental model.

正解：A

解説：
Normalization aids scalability. Options A and C suffer from combinatorial explosion. While JSON can offer flexibility, query performance can be unpredictable.

質問 # 141
......

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