dbt-Analytics-Engineeringトレーニング、dbt-Analytics-Engineering前提条件

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation Techniques	25%	- Common table expressions and subqueries - Refactoring and incremental models - Macros and packages - Jinja templating
Topic 2: Deployment and Orchestration	15%	- CI/CD with dbt Cloud - Environments (dev, staging, prod) - Git version control integration - Jobs and scheduling in dbt Cloud
Topic 3: Testing and Documentation	20%	- Documentation generation - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests
Topic 4: dbt Fundamentals	15%	- dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices
Topic 5: Models	25%	- Sources and references - Writing and managing SQL models - Snapshots - Materializations (table, view, ephemeral, incremental) - Seeds

>> dbt-Analytics-Engineeringトレーニング <<

dbt-Analytics-Engineering前提条件 & dbt-Analytics-Engineering資格試験

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dbt Labs dbt Analytics Engineering Certification Exam 認定 dbt-Analytics-Engineering 試験問題 (Q97-Q102):

質問 # 97
Regular test runs reveal inconsistent and intermittent test failures, despite no obvious changes to models or source dat a. What debugging strategies might you use?

A. Use dbfs logging feature to add detailed output to the test execution.
B. Temporarily switch to a more consistent testing environment.
C. Consider if external dependencies used within dbt tests are themselves unstable.
D. Examine data warehouse performance metrics for resource constraints during test runs.

正解：A、C、D

解説：
Each helps track down potential causes of non-deterministic test behavior. D might be too drastic early on.

質問 # 98
You inherit a project with a monolithic "super-model" performing many transformations. Why might refactoring this into smaller, interconnected models be beneficial?

A. Increases potential for parallel execution within your data warehouse.
B. Enables faster debugging and troubleshooting by isolating issues.
C. Reduces the risk of a single point of failure impacting the entire data flow.
D. All of the above.

正解：D

解説：
All of these are valid reasons for refactoring overly complex models in a dbt project!

質問 # 99
You want to implement a macro that displays a warning icon next to models in the DAG that haven't been tested. Which of the following would be necessary to make this work?

A. A custom CSS class for the warning icon and a way to apply it dynamically using the macro.
B. The ability for the macro to query the results of test executions from the dbt run-
C. Configuration in your dbt_projectyml file to associate models with the relevant tests.
D. All of the above.

正解：D

解説：
A: You'll need CSS to style the warning icon- B. The macro needs to determine if the associated tests for each model have passed or failed. C: dbt needs a way to understand which tests are related to which models for the macro to function correctly.

質問 # 100
While setting up profiles.yml, a colleague suggests hardcoding database passwords directly. You propose alternative approaches for the sake of security. Which of the following are valid arguments in favor of your approach?

A. Credentials often need to be rotated, and hardcoding them would make updates cumbersome.
B. Using environment variables or secrets managers improves maintainability and auditability of credentials.
C. Hardcoded credentials can be accidentally committed to version control, posing a security risk.
D. All of the above.

正解：D

解説：
A: Version control should never contain sensitive data. B. Security practices often necessitate credential rotation. C: Centralizing credentials allows for more secure and controlled management

質問 # 101
(Multiple Select)

A. You need to frequently load incremental data using dbt's snapshot feature.
B. Downstream Bl dashboards rely on up-to-date data transformed by your dbt models-
C. Upstream data sources update on a predictable cadence (e.g., daily extracts).
D. Your data warehouse has time-based pricing tiers, and you need models to refresh during off-peak hours.

正解：B、C

解説：
B aligns model runs with data availability. C ensures Bl dashboards reflect the latest data. A is cost optimization, not a core freshness requirement. D snapshots are more about change tracking than regular updates.

質問 # 102
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