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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| Topic 2: Developing dbt Models | 20% | - Model design and structure
|
| Topic 3: Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution |
| Topic 4: Managing Data Pipelines | 15% | - Deployment strategies - Pipeline orchestration - CI/CD integration |
| Topic 5: Debugging and Error Resolution | 15% | - Debugging techniques - Identifying modeling errors - Resolving data quality issues |
| Topic 6: Creating and Maintaining Documentation | 10% | - Generating documentation - Documentation standards - Descriptions and metadata |
| Topic 7: External Dependencies | 10% | - Using packages - External sources integration - Managing snapshots |
| Topic 8: Leveraging dbt State | 5% | - State-aware operations - State management |
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NEW QUESTION # 101
You discover that an essential dbt macro behaves differently between your development environment and production due to a subtle database version discrepancy. Which debugging approach would be most efficient?
Answer: A,B,D
Explanation:
Each of these helps identify the root cause:A provides runtime insight into the issue. C lets you surface the relevant version for comparison. D allows for reproducible testing.
NEW QUESTION # 102
You've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?
Answer: A,C,D
Explanation:
B: Disabling expensive computations conditionally can boost performance for production environments. C: Snapshots pre-compute values, potentially improving DAG loading times. D: Reducing the amount of data retrieved by the macro could help with performance.
NEW QUESTION # 103 
Answer:
Explanation:
Explanation:
Information Type
Retrieved From
Singular tests
.sql files
Column data types
Data platform information schema
Generic tests
.yml configuration
SQL code
C. .sql files
Column descriptions
.yml configuration
Model dependencies
.sql files
The dbt docs command compiles metadata about your project by gathering information from three primary sources: your warehouse's information schema, your YAML configuration files, and your SQL model files. Understanding which metadata comes from which source is essential for debugging and for effective documentation practices.
Singular tests live inside .sql files within the /tests directory. Since dbt renders these tests directly from SQL files, their definitions appear in documentation sourced from that location.
Column data types come from the warehouse itself. dbt introspects the data platform information schema to retrieve actual types because dbt does not infer or define column types-only the warehouse does.
Generic tests (e.g., unique, not_null, accepted_values) are declared in .yml files. These YAML definitions contain test configurations, descriptions, and parameters, which dbt uses to document and execute these tests.
SQL code for models is naturally sourced from .sql files where the models are defined. This includes logic such as SELECT statements, CTEs, and transformations.
Column descriptions are written exclusively in .yml files. dbt never extracts descriptions from SQL comments-only from YAML.
Model dependencies come from the ref() and source() calls inside .sql model files, which dbt parses to build the DAG.
NEW QUESTION # 104
(Multiple Select)
Answer: B,C
Explanation:
model-paths and test-paths specify project directory structures- Options C would be within a model configuration, and D could be in a profile.
NEW QUESTION # 105
Which two configuration items can be defined under models: in your dbt_project.yml file?
Choose 2 options.
Answer: C,E
Explanation:
The correct answers are A: schema and C: tags.
In dbt, the dbt_project.yml file is the central configuration file that defines model-level settings. Under the models: section, you can specify a wide range of model configurations such as schema, materialized, tags, alias, and custom meta fields. The schema configuration allows you to control which database schema a model should be built in, giving analytics engineers the flexibility to organize models by domain or environment. The tags configuration is also valid under models: and is widely used to group models logically for selection, documentation, or orchestration workflows.
Option B (source) is incorrect because sources are defined under YAML files in the sources: section, not under models: in dbt_project.yml. Option D (test) is incorrect because tests must be defined in model or source YAML files, not inside the project configuration. Option E (target) is not a configuration that applies to models; rather, it refers to dbt runtime environments and cannot be configured under the models: block.
dbt's project configuration system ensures that model-level behavior is managed centrally and consistently, and schema and tags are two of the officially supported configuration keys under models:.
NEW QUESTION # 106
......
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