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[General] Effective dbt Labs dbt-Analytics-Engineering Exam Preparation In a Short Time

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【General】 Effective dbt Labs dbt-Analytics-Engineering Exam Preparation In a Short Time

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q87-Q92):NEW QUESTION # 87
You're testing a model containing financial calculations. Your stakeholders require a high degree of accuracy. Which consideration is most important when designing tests for this scenario?
  • A. Ensuring tests cover a comprehensive range of input scenarios and edge cases.
  • B. Implementing a large number of generic tests to provide basic coverage.
  • C. Using floating-point-specific equality checks for calculated values.
  • D. Understanding acceptable tolerances and writing tests to check values within a defined range rather than exact matches.
Answer: A,D
Explanation:
B recognizes that financial calculations often have rounding imprecision, necessitating tolerance-based checks. C is crucial as even minor input variations can propagate into significant errors for financial models. A is generally a bad practice; D focuses on quantity, not the specific quality needed here.

NEW QUESTION # 88
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?
  • A. Employ a secrets management tool and integrate it with your dbt deployment.
  • B. Utilize environment variables to store seed data values.
  • C. Write the seed data into log files, excluding them from version control.
  • D. Create separate seed files and add them to your .gitignore or equivalent.
Answer: D
Explanation:
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.

NEW QUESTION # 89
You've implemented a macro to standardize descriptions across a set of models. After updating the macro and regenerating docs, the changes don't seem to be reflected. What could be the cause?
  • A. Changes to macros sometimes require a full re-compilation of all models for the updates to take effect in the documentation.
  • B. The macro might only be applied to specific model types.
  • C. Your macro implementation has a subtle logic error.
  • D. The documentation generator is caching the old version of the macro.
Answer: A,C,D
Explanation:
A: Errors in macro logic can prevent intended results. B: Similar to other content, macros can be cached, so clearing your browser cache might be needed. C: Due to dependency relationships, macro changes might sometimes require re-compilation of affected models to take full effect in the docs.

NEW QUESTION # 90
You are working on a complex dbt model with many Common Table Expressions (CTEs) and decide to move some of those CTEs into their own model to make your code more modular.
Is this a benefit of this approach?
The new model can be documented to explain its purpose and the logic it contains.
  • A. No
  • B. Yes
Answer: B
Explanation:
Yes, this is a benefit of breaking large CTE-heavy SQL models into modular dbt models. According to dbt and Analytics Engineering best practices, modularity improves clarity, maintainability, and documentation quality. When CTEs remain embedded inside a single large SQL file, their purposes are often unclear, difficult to document, and hard for other developers to reuse. By extracting a logical CTE into its own model, dbt treats it as a first-class resource-meaning it can have its own description, tests, documentation, lineage, and metadata defined in YAML.
dbt's documentation system allows each model to include a description explaining what the transformation does, the assumptions being made, and the expected behavior of the data. This aligns with the Analytics Engineering principle of creating self-documenting pipelines, where transformations are transparent and easier for downstream users to understand.
Additionally, modular models improve lineage visualization in the DAG. Instead of a single model hiding multiple transformation layers, a modular structure reveals how data flows through each intermediate step, helping both debugging and governance. Modularization also enables reusability-other models can reference the intermediate model rather than rebuilding the same logic through duplicated CTEs, supporting DRY (Don't Repeat Yourself) principles.
Therefore, moving CTEs into separate dbt models absolutely provides a documentation benefit and improves the overall engineering quality of the project.

NEW QUESTION # 91
Which of the following scenarios might warrant a git rebase instead of a standard git merge when integrating your changes into the main branch?
  • A. Your branch has diverged significantly from the main branch with unrelated changes.
  • B. You need to resolve complex merge conflicts that arise during a standard merge.
  • C. You only want to include a subset of your feature branch commits in the main branch.
  • D. You want to avoid a merge commit and keep a linear project history.
Answer: A,D
Explanation:
A Rebasing creates a more linear history, making it easier to trace changes over time. D. Rebasing allows for a cleaner integration when a feature branch contains intermingled changes that shouldn't all be merged.

NEW QUESTION # 92
......
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