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Which of the following is true about restricting the usage of models in dbt?
Choose 1 option.
A. Model groups can limit references by other models which aren't in the same group.
B. Restrictions are set by defining the models and Git user groups approved for usage.
C. You must map your data platform roles to groups by specifying the role name in the configurations.
D. Data platform user emails can be used to determine who can reference a model.
Answer: A
Explanation:
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.
NEW QUESTION # 25
The dbt_project.yml file contains this configuration:
models:
+grants:
select: ['reporter']
How can you grant access to the object in the data warehouse to both reporter and bi?
Answer: B
Explanation:
In dbt, grants can be configured globally, at the project level, or directly inside a model using the config() function. When a grant is set in dbt_project.yml, it becomes a base definition, and individual models may extend (append to) or override that configuration.
According to dbt documentation, prefixing a grant with a plus sign (+) means "extend the list defined in higher-level configurations." Thus, if the project-level config already sets:
select: ['reporter']
...and a model needs to add an additional role (here, bi) without removing the existing one, the correct syntax is:
{{ config(grants = { '+select': ['bi'] }) }}
This tells dbt: "Take the existing grant list (reporter) and append bi to it." Option B is incorrect because '+bi' is not a valid grants syntax.
Option C is invalid because grants do not use an inherits parameter.
Option D is invalid because include: is not a grants configuration key.
Therefore, Option A correctly applies dbt's documented merging behavior for grants.
NEW QUESTION # 26
Match the macro to the appropriate hook so that the correct execution steps comply with these rules:
* macro_1() needs to be executed after every dbt run.
* macro_2() needs to be executed after a model runs.
* macro_3() needs to execute before every dbt run.
* macro_4() needs to be executed before a model runs. Answer:
Explanation:
Explanation:
Hook 1
on-run-end: "{{ macro_x() }}"
The Answer:
macro_1
Hook 2
models:
<my_dbt_project>:
post-hook: "{{ macro_x() }}"
The Answer:
macro_2
Hook 3
on-run-start: "{{ macro_x() }}"
The Answer:
macro_3
Hook 4
{{
config(
pre-hook: "{{ macro_x() }}"
)
}}
macro_4
dbt supports run-level hooks and model-level hooks.
Run-level hooks fire once per invocation, while model- level hooks fire around each individual model.
on-run-end is a run-level after hook that executes once after the entire dbt command completes.
Because macro_1() must run after every dbt run, it correctly belongs here.
The post-hook configured under the models: section runs after each model in that scope finishes building.
This matches the requirement for macro_2() to execute after a model runs.
on-run-start is a run-level before hook and fires once before dbt begins executing any models for that command, making it the right place for macro_3() which must run before every dbt run.
Finally, the pre-hook specified inside a model's config() block runs before that specific model is built.
Since macro_4() must execute before a model runs, it belongs in the pre-hook configuration.
Thus the correct mapping is:
* on-run-end # macro_1
* model post-hook # macro_2
* on-run-start # macro_3
* model pre-hook # macro_4.
NEW QUESTION # 27
28. Consider this DAG:
* model_a # model_c # model_e
* model_b # model_d # model_f
(With model_c and model_d both feeding into the final layer.)
You execute:
dbt run --fail-fast
in production with 2 threads. During the run, model_b and model_c are running in parallel when model_b returns an error.
Assume there are no other errors in the model files, and model_c was still running when model_b failed.
Which model or models will successfully build as part of this dbt run? Choose 1 option.
A. model_a, model_c, model_d, model_e, model_f
B. model_a, model_c
C. model_a
D. model_a, model_c, model_e
Answer: B
Explanation:
The --fail-fast flag tells dbt to stop scheduling any new nodes as soon as one node fails. Importantly, dbt does not kill models that are already running; in-flight nodes are allowed to finish.
Here's what happens step by step with 2 threads:
* Roots model_a and model_b start first.
* model_a finishes successfully. That makes model_c eligible to run.
* dbt now runs model_b and model_c in parallel.
* While they are running, model_b fails.
* Because --fail-fast is set, dbt immediately stops scheduling any additional models (like model_d, model_e, or model_f).
* model_c was already running when model_b failed, so it is allowed to complete successfully.
Downstream models of either branch (model_d, model_e, and model_f) never start, because fail-fast prevents any further nodes from being queued after the first failure.
So, the only models that successfully build during this run are:
* model_a (completed before model_b failed)
* model_c (already running at the time of failure and allowed to finish) Hence the correct choice is B: model_a, model_c.
NEW QUESTION # 28
Which two configurations can be applied to a dbt test?
Choose 2 options.
A. materialized
B. tags
C. enabled
D. on_schema_change
E. persist_docs
Answer: B,C
Explanation:
The correct answers are B: tags and C: enabled.
dbt tests-both generic and singular-support a limited but clearly defined set of configurations. Two of the supported configurations are tags, which allow grouping and selecting tests using dbt's selector syntax, and enabled, which can be set to true or false to control whether a test should run. These configurations are commonly used to manage large test suites, such as disabling slow tests in development or tagging tests for CI pipelines.
Option A (on_schema_change) is a model-only configuration and cannot be applied to tests.
Option D (materialized) applies only to models, not tests, since tests compile into queries that evaluate failures rather than physical objects.
Option E (persist_docs) applies to models and sources for documentation purposes. Tests do not support persisted documentation properties beyond what appears in test metadata.
dbt's documentation makes clear that tests inherit only a small subset of configurations compared to models- primarily enabled, tags, severity, and custom test arguments. Among the options provided, only tags and enabled are valid and supported.
Thus, the correct answers are B and C.
NEW QUESTION # 29
......
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