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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developing dbt Models | 20% | - Model design and structure
|
| Topic 2: Creating and Maintaining Documentation | 10% | - Generating documentation - Descriptions and metadata - Documentation standards |
| Topic 3: External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Topic 4: Managing Data Pipelines | 15% | - Deployment strategies - Pipeline orchestration - CI/CD integration |
| Topic 5: Implementing dbt Tests | 10% | - Test configuration and execution - Custom tests - Built-in tests |
| Topic 6: Leveraging dbt State | 5% | - State management - State-aware operations |
| Topic 7: dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| Topic 8: Debugging and Error Resolution | 15% | - Resolving data quality issues - Identifying modeling errors - Debugging techniques |
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NEW QUESTION # 25
A performance-critical model depends on multiple complex transformations that would benefit from being pre-computed. Direct querying of this model should be as fast as possible. Which materialization is likely the best choice?
Answer: C
Explanation:
Explanation: Tables store materialized results on disk. When pre-calculated, they offer the fastest query response time, making them suitable for performance-sensitive models.
NEW QUESTION # 26
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: C
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 # 27
You are using seeds in your dbt project for static data lookups. During a dbt seed run, you encounter an error indicating a duplicate primary key value. What are the likely consequences?
Answer: B,C
Explanation:
A and C are correct. Most databases enforce unique primary keys, so the whole dbt seed operation would fail to preserve data integrity. B is incorrect. Seed operations are typically "all or nothing". D is incorrect. dbt treats seeds as important as other model types.
NEW QUESTION # 28
You suspect discrepancies in a critical model. To troubleshoot, you need to understand the order of model execution, but the DAG is complex. What would be the BEST strategy to get this information?
Answer: B
Explanation:
While other options provide some information, dbt docs generate creates comprehensive documentation including a visual DAG to show execution flow clearly.
NEW QUESTION # 29
You've built a macro to implement a common custom test pattern. When using this macro in multiple models, you realize the tests are running much slower than expected. What might be the cause?
Answer: B,D
Explanation:
Inefficient macro logic would be amplified with wide reuse. Having numerous tests, even simple ones, can add up. C is unlikely unless the macro directly references downstream models.
NEW QUESTION # 30
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