dbt-Analytics-Engineering Prüfung - dbt-Analytics-Engineering Praxisprüfung

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Objectives
Documentation and Lineage	- Data lineage understanding 1. Directed acyclic graph (DAG) 2. Dependency tracking with ref() - dbt documentation system 1. Model descriptions and metadata 2. Auto-generated docs site
Analytics Engineering Foundations	- SQL proficiency for analytics 1. Data modeling in SQL 2. Joins, aggregations, and window functions - Modern data stack concepts (ELT vs ETL) 1. Warehouse-centric transformation workflows 2. Role of dbt in analytics engineering
dbt Core Concepts	- Project structure and configuration 1. Packages and dependencies 2. dbt_project.yml configuration - Models and materializations 1. Ref and source functions 2. Views, tables, incremental models
Deployment and Orchestration	- Environments and workflows 1. Development vs production environments 2. Version control with Git - Running dbt in production 1. dbt Cloud and job scheduling 2. CI/CD integration patterns
Testing and Data Quality	- Data validation strategies 1. CI-based validation workflows 2. Schema testing practices - Built-in and custom tests 1. Custom SQL tests 2. Generic tests (unique, not null, relationships)

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dbt Labs dbt Analytics Engineering Certification Exam dbt-Analytics-Engineering Prüfungsfragen mit Lösungen (Q81-Q86):

81. Frage
You have a dbt macro that generates a list of models to execute dynamically. Which dbt command should you use in conjunction with this macro to actually run those models?

A. dbt parse to pre-compile the project graph, allowing model selection by reference.
B. dbt build to create a task graph, extracting model nodes, and running them as needed.
C. dbt Is to retrieve project metadata and feed it into your custom execution logic.
D. dbt debug to step through model execution while using the macro's output

Antwort: A

Begründung:
A makes the dbt graph available, including macros, during execution- B gets metadata, but not for execution. C helps troubleshoot, but not run macros. D isn't designed for dynamic macro output

82. Frage
A complex transformation in your dbt project pulls data from an external API. Due to rate limits, this API endpoint cannot be queried frequently in production. How could you handle development and testing of this transformation effectively?

A. Utilize dbt snapshots to capture a static set of API data for use during development and testing.
B. Use environment variables to control API query frequency or switch between live and test data sources.
C. Set up a development environment that utilizes a mock API to simulate external data.
D. All of the above could be part of a strategy.

Antwort: D

Begründung:
A: Mocks enable testing without impacting the production API- B: Environment variables allow for flexible configuration. C: Snapshots provide static samples for testing in isolation.

83. Frage
After configuring a dbt package, you notice its models depend on a source table absent from your warehouse. How do you typically address this?

A. Update your source definitions to include the required table.
B. Override the package model's SQL query to reference an existing table in your warehouse.
C. File an issue or submit a pull request to the package's repository.
D. Rewrite the package's model from scratch within your project to remove the dependency.

Antwort: B

Begründung:
Start by fulfilling the package's dependencies. If the table is truly generic, then customizing the SQL (B) might be the next step.

84. Frage
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?

A. Run git rebase main followed by git commit -amend.
B. Manually copy your files into a new branch, commit, and issue a pull request.
C. git cherry-pick specific commits from your branch and reapply them.
D. Use git rebase -i (interactive rebase) to squash commits together.

Antwort: D

Begründung:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history

85. Frage
You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?

A. Use a third-party dbt package that enhances the presentation of source descriptions in documentation.
B. Utilize Markdown within your descriptions to improve readability with headings and line breaks-
C. Split your source description into multiple properties (e.g., description_short and description_long)
D. Enforce a strict character limit on source descriptions-

Antwort: A,B,C

Begründung:
B: Markdown syntax can improve long description readability within the generated docs. C: Third-party packages may offer customized formatting for source descriptions. D: Creating separate properties can provide an alternative way to manage both short and longer versions of a description.

86. Frage
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