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| Section | Weight | Objectives |
|---|---|---|
| Developing dbt Models | 20% | - Model design and structure
|
| Implementing dbt Tests | 10% | - Test configuration and execution - Built-in tests - Custom tests |
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Debugging techniques - Identifying modeling errors |
| dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Documentation standards - Generating documentation |
| Managing Data Pipelines | 15% | - CI/CD integration - Deployment strategies - Pipeline orchestration |
| External Dependencies | 10% | - Using packages - External sources integration - Managing snapshots |
| Leveraging dbt State | 5% | - State management - State-aware operations |
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NEW QUESTION # 295
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: B
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 # 296
Which two are true about dbt tests?
Choose 2 options.
Answer: B,E
Explanation:
The correct answers are C and D.
dbt supports two main categories of tests: generic tests and singular tests. Generic tests are defined in YAML and applied to models or columns, while singular tests are written as SQL files placed in the /tests/ directory.
This makes Option C correct-singular tests must be created as .sql files inside that folder, and dbt will execute each file as a test query.
Option D is also correct. dbt includes four built-in generic tests: unique, not_null, relationships, and accepted_values. These are considered "native" tests and are available without requiring any additional packages. They cover the most common data quality checks and are applied through YAML configurations on models or columns.
Option A is incorrect because dbt does not automatically apply any tests. All tests-native or custom-must be explicitly defined in YAML or created manually. Nothing is inferred from primary keys.
Option B is incorrect because dbt's package hub contains community packages, not the list of native tests.
Native tests are documented directly within dbt's core functionality.
Option E is incorrect because the constraints configuration is used to create database-level constraints on supported warehouses, not to run dbt tests. Tests still require YAML test definitions or .sql files.
Thus, only C and D accurately describe dbt's testing behavior.
NEW QUESTION # 297
(Multiple Select)
Answer: A,B,C
Explanation:
All but option D could lead to this specific error. Tests wouldn't directly cause a column mismatch, but they might help you catch the error.
NEW QUESTION # 298
13. An analyst on your team has informed you that the business logic creating the is_active column of your stg_users model is incorrect.
You update the column logic to:
case
when state = 'Active'
then true
else false
end as is_active
Which test can you add on the state column to support your expectations of the source data? Choose 1 option.
Answer: D
Explanation:
The purpose of this question is to determine how to validate that the input values in the state column support the business logic that determines the is_active field. Since the logic checks whether state = 'Active', it is critical that the state column only contains values that the business process expects. In the example shown, acceptable states appear to be: 'active', 'churned', and 'trial'.
The correct way to enforce this expectation is to apply an accepted_values test on the state column. This ensures that any unexpected state (e.g., 'inactive', 'pending', 'deleted', or NULL) will cause the test to fail, alerting the team that the upstream system is producing unexpected or invalid values. Additionally, adding not_null ensures every user record contains a valid state.
Option A is the only configuration that applies the accepted values test to the correct column (state) and reflects the expected domain of values.
Options B and D incorrectly apply tests to the derived column is_active, not the source column that needs validation. Option C only checks nullability and uniqueness, which does not validate the range of allowed values and thus does not protect the business logic.
Therefore, Option A is the only correct answer.
NEW QUESTION # 299
Your dbt project contains sensitive dat
a. You need to load seed data (e.g., lookup tables) while ensuring it's not accidentally committed to version control. How can you best achieve this?
Answer: A
Explanation:
Seed files designed for local use should be kept out of version control. While the other options have merits, they don't directly prevent sensitive data from being committed.
NEW QUESTION # 300
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