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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: External Dependencies | 10% | - Using packages - External sources integration - Managing snapshots |
| Topic 2: Debugging and Error Resolution | 15% | - Identifying modeling errors - Resolving data quality issues - Debugging techniques |
| Topic 3: Leveraging dbt State | 5% | - State management - State-aware operations |
| Topic 4: Creating and Maintaining Documentation | 10% | - Documentation standards - Descriptions and metadata - Generating documentation |
| Topic 5: dbt Models Governance | 15% | - Version control integration - Project organization and structure - Naming conventions and standards |
| Topic 6: Managing Data Pipelines | 15% | - CI/CD integration - Deployment strategies - Pipeline orchestration |
| Topic 7: Implementing dbt Tests | 10% | - Test configuration and execution - Custom tests - Built-in tests |
| Topic 8: Developing dbt Models | 20% | - Model design and structure
|
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NEW QUESTION # 214
You want to compare the current version of a file in your working directory to the most recently committed version. Which Git command would be most helpful?
Answer: A
Explanation:
git diff shows line-by-line differences between versions, letting you compare your working version to the committed one (HEAD).
NEW QUESTION # 215
You discover a vulnerability in a third-party lineage-enhancing dbt package you use. Where might you first look for information and potential fixes?
Answer: D
Explanation:
The package maintainer is the primary source for updates and vulnerability fixes. However, other resources like A & C might provide additional information or community solutions.
NEW QUESTION # 216
You get a compilation error: "Configuration key 'enabled' not found". Where in your dbt_project.yml would you start looking to fix this, and why?
Answer: C
Explanation:
Since it's a generic error, start with the most common location - model configurations. The others might be valid in certain scenarios.
NEW QUESTION # 217
You want to restrict which models can refer to a specific model.
How can this be done?
Choose 1 option.
Answer: B
Explanation:
The correct answer is A: Add model groups and set access to private.
dbt's access control system is designed to restrict which models may reference others purely within the dbt project itself, independent of data warehouse permissions. This system uses model groups and an access property, which can be set to public, protected, or private. Setting a model to private means that only models within the same group are allowed to reference it. Any attempts to reference the model from outside the group will cause a compilation error, enforcing strong boundaries between domains or data products within a dbt project.
Option C (protected) is incorrect because protected allows references from other groups but requires explicit configuration-the intention is stewardship, not restriction. It does not fully restrict referencing.
Option B is incorrect because dbt model access control is not tied to warehouse permissions. Warehouse- level grants cannot control dependency references inside the dbt DAG.
Option D is irrelevant to access control. Versioning does not restrict references; it only manages updates to published models.
Therefore, the only correct way to restrict which models can reference another model is by placing it into a model group and marking it as private, which enforces strict internal usage boundaries.
NEW QUESTION # 218
You want to add new columns to an existing model with a new version.
What will trigger warnings for removal of older model versions?
Choose 1 option.
Answer: D
Explanation:
dbt includes built-in support for model versioning and controlled deprecation. When you introduce a new version of a model, dbt will only warn about the old version being removed if the old version explicitly includes a deprecation_date configuration. This parameter signals to dbt that the model is scheduled for removal after a certain date, allowing dbt to produce structured warnings to users and downstream systems.
These warnings serve as a formal notice that the old version should no longer be referenced and will eventually be removed.
Option A, simply creating a new version, does not trigger warnings by itself. dbt allows multiple versions to coexist without any deprecation messaging unless you explicitly configure it. Option C (enabled: false) disables the model entirely, which is not a graceful deprecation strategy and does not result in version- removal warnings. Option D (adding "deprecated" in the description) is only informational text and has no functional effect on dbt's deprecation system.
Thus, only B-setting deprecation_date on the old version-activates dbt's official deprecation warning mechanism, making it the correct answer.
NEW QUESTION # 219
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