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| Section | Weight | Objectives |
|---|---|---|
| Implementing dbt Tests | 10% | - Custom tests - Test configuration and execution - Built-in tests |
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Documentation standards - Generating documentation |
| Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| Debugging and Error Resolution | 15% | - Debugging techniques - Identifying modeling errors - Resolving data quality issues |
| Developing dbt Models | 20% | - Model optimization
|
| External Dependencies | 10% | - External sources integration - Managing snapshots - Using packages |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| dbt Models Governance | 15% | - Version control integration - Naming conventions and standards - Project organization and structure |
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NEW QUESTION # 120
Examine how freshness is defined at the database level:
- name: raw
database: raw
freshness: # default freshness
warn_after: {count: 12, period: hour}
error_after: {count: 24, period: hour}
loaded_at_field: _etl_loaded_at
How can one table from the source raw be excluded from source freshness?
Answer: B
Explanation:
In dbt, source freshness can be defined at the source level (affecting all tables) or overridden at the individual table level. When freshness is declared at the source root, dbt applies the freshness policy to every table within that source unless a specific table opts out. The official dbt documentation states that setting:
freshness: null
at the table level disables freshness checks for that specific table, even when the source itself defines default freshness rules. This is the only documented way to exclude an individual table from a source freshness policy.
Option B is incorrect because tables can override and disable freshness even when the parent source defines it. dbt's configuration hierarchy supports overriding child-level settings on a per-resource basis.
Option C (loaded_at_field: null) does not disable freshness-it simply removes the loaded_at_field, which would cause dbt to error because a freshness check requires a timestamp field to operate.
Option D (error_after: null) only removes the error threshold but does not disable freshness. The table would still participate in freshness checks and could trigger warnings.
Thus, the correct and dbt-supported method to exclude a table from freshness is to explicitly set:
freshness: null
making A the correct answer.
NEW QUESTION # 121
Your fct_orders model depends on both raw_sales and stg_customers. Due to upstream changes, the customer_id column in raw_sales is now named cust_id. How could you manage this change with minimal disruption?
Answer: A
Explanation:
The ref() function is designed for these situations. It makes your models adaptable to upstream changes. Option A would work but creates maintenance overhead.
NEW QUESTION # 122
Your model has a contract on it.
When renaming a field, you get this error:
This model has an enforced contract that failed.
Please ensure the name, data_type, and number of columns in your contract match the columns in your model's definition.
| column_name | definition_type | contract_type | mismatch_reason |
|-------------|------------------|----------------|-----------------------|
| ORDER_ID | TEXT | TEXT | missing in definition |
| ORDER_KEY | TEXT | | missing in contract |
Which two will fix the error? Choose 2 options.
Answer: A,C
Explanation:
dbt model contracts enforce that the column names, data types, and number of columns defined in the contract exactly match the columns produced by the compiled SQL. If any column appears in one location (the contract or the SQL) but not in the other, dbt raises an enforcement error.
In this scenario, the error message shows:
* ORDER_ID is missing in the model definition, meaning the SQL no longer contains a column named order_id, but the contract still expects it.
* ORDER_KEY is missing in the contract, meaning the model SQL now contains a new column, but this column has not been added to the contract.
To fix the mismatch, you must remove columns from the contract that no longer exist in the SQL and add to the contract any new columns that now appear in the SQL.
Therefore:
* Option A - Remove order_id from the contract - is correct because the column no longer exists in the model SQL.
* Option D - Add order_key to the contract - is correct because the SQL now produces this column.
Options B and C incorrectly alter the wrong side of the definition, and Option E would create a mismatch in the opposite direction.
Thus, the correct fixes are A and D.
NEW QUESTION # 123
Which explanation describes how dbt infers dependencies between models?
Choose 1 option.
Answer: D
Explanation:
The correct answer is A: Information is gathered from the use of source and ref macros.
dbt determines the dependency graph - the DAG - by analyzing calls to ref() and source() inside model SQL files. These macros explicitly declare relationships between models. When a developer writes ref ('orders'), dbt interprets this as: "the current model depends on the orders model." Similarly, source() indicates dependencies on upstream raw data sources. This declarative approach allows dbt to build a structured and deterministic DAG without scanning SQL for implicit table references.
Option B is incorrect because dbt does not query database objects to infer dependencies; it resolves dependencies at compile time through metadata generated from model files. Option C is incorrect because dbt intentionally does not parse SQL to detect table names-this would be brittle and error-prone across warehouses. Instead, dbt requires explicit references to maintain reliability. Option D is incorrect because YAML files define metadata about models and sources but do not create dependency relationships between them.
Thus, the dependency graph is built exclusively by reading ref() and source() macro calls, which ensures clarity, correctness, and maintainability within the analytics engineering workflow.
NEW QUESTION # 124
(Multiple Select)
Answer: C,D
Explanation:
Visualization and incremental execution aid debugging. Custom logging might be necessary but is more time-consuming to implement. A full refresh isn't targeted for debugging.
NEW QUESTION # 125
......
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