Quiz Professional dbt-Analytics-Engineering - Online dbt Analytics Engineering Certification Exam Bootcamps

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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
(Multiple Select)

Answer: A,D

Explanation:
Verifying source data and stepwise debugging are key. Tests help but come later. Changing the materialization is unlikely to directly address the null-value issue.


NEW QUESTION # 51
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.

Answer: B

Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.


NEW QUESTION # 52
You're using the dbt-utils.get_column_values macro to list out the unique values from a specific source column in the documentation. However, the generated docs don't display any results. Which of the following reasons are most likely?

Answer: A,B

Explanation:
A: Some macros are designed specifically for model columns and may not function as expected with source columns. B: Macros might depend on data from a recent dbt run operation to generate their output. C: It's always possible that the macro has a bug or your implementation is not quite correct.


NEW QUESTION # 53
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?

Answer: A,B,C

Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.


NEW QUESTION # 54
While explaining dbt concepts to a colleague, they ask, "Don't macros just add an extra layer of complexity?" How would you address their concern?

Answer: A

Explanation:
Acknowledge the potential increase in upfront complexity but emphasize the long-term benefits in reducing code duplication, improving readability, and enhancing maintainability.


NEW QUESTION # 55
......

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