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| Section | Weight | Objectives |
|---|---|---|
| Leveraging dbt State | 5% | - State-aware operations - State management |
| Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| External Dependencies | 10% | - External sources integration - Managing snapshots - Using packages |
| Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors |
| Implementing dbt Tests | 10% | - Built-in tests - Test configuration and execution - Custom tests |
| Developing dbt Models | 20% | - Model design and structure
|
| dbt Models Governance | 15% | - Project organization and structure - Version control integration - Naming conventions and standards |
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Generating documentation - Documentation standards |
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NEW QUESTION # 142
(Multiple Select)
Answer: A,B
Explanation:
Contributing to open-source is great, but you likely need an immediate fix. Overriding gives you control while addressing the issue upstream. Forking is feasible, but the maintenance burden might outweigh the benefits.
NEW QUESTION # 143
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 # 144
Which of the following scenarios would likely require you to use the -no-compile flag with the dbt docs generate command?
Answer: A
Explanation:
The -no-compile flag skips model compilation and is useful when you only want to update the docs without the risk of model compilation affecting a production environment.
NEW QUESTION # 145
You've introduced a new series of dbt models and want to run them in isolation to verify results before integrating them into the main build process. Which dbt command options and flags are likely to be useful?
Answer: A,C
Explanation:
A and C provide precise selection mechanisms. B is for exclusion, not focused execution- D is about tests, not model runs-
NEW QUESTION # 146
Choose a correct command for each statement.
Answer:
Explanation:
Explanation:
Will always point to the latest version of the source schema
Correct Match: # defer
Allows to use objects built in the target schema with any downstream tool Correct Match: # dbt clone
3## Allows to safely modify objects built in the target schema
Correct Match: # defer
4## Is a point-in-time operation
Correct Match: # dbt clone
defer and dbt clone serve very different purposes in dbt, and understanding their behavior is essential for choosing the right command.
The --defer flag tells dbt to use already-built objects from a previous environment (often production) instead of rebuilding them. Because it always references the existing target schema's most recent objects, it "always points to the latest version of the source schema." Since no objects are overwritten when using defer, it also
"allows safely modifying objects built in the target schema"-your development environment uses production objects without altering them.
By contrast, dbt clone creates a point-in-time copy of existing relations. This cloned schema is static; it does not auto-update when source data changes. Therefore, clone is classified as a "point-in-time operation." Since clone copies physical tables/views into a new schema, downstream tools (BI dashboards, ML pipelines) can safely query the cloned environment without affecting production, making "allows to use objects built in the target schema with any downstream tool" the correct match.
Thus, defer is used for logic substitution without copies, while clone is used for replicable, point-in-time snapshots.
NEW QUESTION # 147
......
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