dbt Labs dbt-Analytics-Engineering Exam Dumps - Actual Questions Answers

The correct answer is C: Model groups can limit references by other models which aren’t in the same group.

According to the dbt documentation, dbt provides a formal access-control mechanism for models through model groups and the access property. Models can be assigned to groups, and each model can be marked as public, protected, or private. The purpose of this system is to create clear boundaries within a project, making it possible to control which models are allowed to reference others. Specifically, dbt enforces rules where models in a group can restrict references from models outside that group, which helps maintain modularity and prevents accidental coupling of unrelated data layers. This is extremely valuable in large analytics engineering projects where teams manage different domains or data products.

Option A is incorrect because dbt does not use Git user groups or any form of identity-based access control for model usage. Option B is incorrect because dbt does not integrate with warehouse roles for controlling model references—access is handled strictly within the dbt project’s metadata. Option D is incorrect because user emails or platform identities have no role in determining which models can reference others. dbt enforces usage rules only through its metadata-driven grouping and access configuration system.

The correct answer is A: The freshness check can be disabled with freshness: null.

In dbt, freshness checks are controlled by the freshness configuration block within a source definition. If a project includes a default freshness configuration, but you want to turn off freshness checking for a specific table, you can explicitly set freshness: null. This overrides inherited settings and tells dbt not to compute or evaluate freshness for that object.

Option B is incorrect because dbt evaluates all configured sources before finishing; it does not exit after the first failure. Instead, it completes the run and reports pass/warn/error statuses according to the configured thresholds.

Option C is incorrect because the placement of freshness checks is flexible. Some teams run dbt source freshness first to validate data arrival before running models; others run it separately or at the end of a workflow. There is no requirement that it always be last.

Option D is incorrect because dbt build does not automatically run freshness checks. Freshness checks are performed only when dbt source freshness is executed explicitly or configured in a dbt Cloud job step.

Therefore, A is the only statement that correctly describes the behavior of source freshness checks.

The correct answer is B: Refer to a manifest and utilize the --defer and --state flags.

According to dbt’s official documentation, the --defer flag enables developers to defer the resolution of model references (ref(), source()) to an existing manifest—commonly the one generated from a production run. When this flag is used along with --state, dbt reads the prior project state and uses the already-built production models instead of rebuilding upstream dependencies. This is essential because production models are typically large and compute-intensive. By deferring to production artifacts, developers can test only the models they have modified, dramatically reducing warehouse costs and speeding up development cycles.

This approach is foundational to dbt’s recommended development workflow, especially when working with CI/CD or large DAGs. It preserves the integrity of the dependency graph while avoiding unnecessary recomputation.

Option A violates dbt best practices by removing dependency awareness. Option C is unnecessary operational overhead and does not integrate with dbt’s state management. Option D is incomplete because --state alone does not prevent upstream model execution—--defer is required to substitute those models with existing artifacts.