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| Section | Objectives |
|---|---|
| Topic 1: dbt Core Concepts | - Project structure and configuration
|
| Topic 2: Analytics Engineering Foundations | - Modern data stack concepts (ELT vs ETL)
|
| Topic 3: Testing and Data Quality | - Built-in and custom tests
|
| Topic 4: Deployment and Orchestration | - Running dbt in production
|
| Topic 5: Documentation and Lineage | - Data lineage understanding
|
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NEW QUESTION # 270
A developer imports a package from a private repository called timeformat for use within their project.
Which statement is correct? Choose 1 option.
Options:
Answer: A
Explanation:
The only correct statement is Option C, because dbt allows you to override the schema for a package's models directly within your project's dbt_project.yml file. Packages behave just like subfolders within your own project, and therefore the standard configuration pattern applies:
models:
timeformat:
+schema: timeseries
This correctly overrides the package's default schema, allowing all models within the timeformat package to be built into the timeseries schema.
Why the other options are incorrect
Option A
Local packages can be added using a path reference, but that is not what a private Git repository refers to. A private repo must be added using git: plus SSH/HTTPS URL, not local: unless the developer literally cloned the repo into a local directory. The question specifically describes a private repository, so this is not the correct statement.
Option B
Packages are installed using dbt deps, not dbt build.
dbt build only runs models, tests, and snapshots; it does not install dependencies.
Option D
Including a version or revision is allowed for private Git-based packages. You can specify branches, tags, or commit hashes safely in packages.yml.
NEW QUESTION # 271
You have a dbt model that depends on several upstream source tables. One of these source tables is occasionally updated very late, causing your job to fail if triggered at the usual time. What configuration could mitigate this?
Answer: C,D
Explanation:
C provides a proactive check directly before execution. D makes the model more resilient to source data timing issues. A wouldnt prevent the job from running unless you modify its execution setup. B is overkill and inefficient.
NEW QUESTION # 272
You're refactoring a complex dbt model and realize breaking it down into smaller models would improve readability. However, you're concerned about performance overhead due to the additional materializations. How would you address this concern?
Answer: B
Explanation:
Don't optimize prematurely. Profiling lets you identify the most performance-critical parts of your model before changing materializations. The other options may help but targeted optimizations are key.
NEW QUESTION # 273
You're working in a dbt Cloud development environment. You make a change to a model definition that accidentally introduces an invalid column reference. What's the likely sequence of events when you trigger a dbt run?
Answer: A
Explanation:
dbt performs pre-execution validation, catching many schema errors early. Behavior in C is less likely and D shouldnt be so inconsistent.
NEW QUESTION # 274
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 # 275
......
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