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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| Topic 2: Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors |
| Topic 3: Developing dbt Models | 20% | - Model optimization
|
| Topic 4: Creating and Maintaining Documentation | 10% | - Documentation standards - Descriptions and metadata - Generating documentation |
| Topic 5: External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Topic 6: Implementing dbt Tests | 10% | - Test configuration and execution - Built-in tests - Custom tests |
| Topic 7: Leveraging dbt State | 5% | - State management - State-aware operations |
| Topic 8: dbt Models Governance | 15% | - Version control integration - Project organization and structure - Naming conventions and standards |
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NEW QUESTION # 276
Ignoring indentation, arrange these YAML code snippets in the correct order to generate descriptions on the source, table, and column:
Answer:
Explanation:
Explanation:
Here is the correct sequence:
Correct Order
* sources:
* - name: blue (source definition)
* tables:
* - name: yellow (table definition)
* columns:
* - name: red (column definition + tests)
In dbt, source documentation follows a strict YAML hierarchy. At the top level, the sources: key declares that you are defining source metadata. Inside this block, each source is listed as an item beginning with - name:.
Therefore, the snippet describing the source (blue) must come immediately after sources:.
Next, dbt requires a tables: block inside each source to define the raw tables belonging to that source. Thus, the snippet containing tables: must follow the source definition. The table description (yellow) is then placed directly under this key because it represents a table within that source.
Finally, dbt allows column-level metadata under a columns: block inside a table definition. Therefore, columns: appears next, followed by the snippet defining a specific column (red) which includes both a description and tests (unique, not_null).
Arranging the YAML in this hierarchical order ensures dbt can correctly assign documentation to the source object, the table object, and the individual column. Misordering these snippets would break the structure and prevent dbt from generating documentation properly.
NEW QUESTION # 277
Your dbt project pulls data from a SaaS application using a third-party adapter. You observe performance issues. Where might you start optimizing?
Answer: B
Explanation:
Third-party adapters often introduce bottlenecks. Start with optimizing whats specific to the adapter The others might help in some cases but are less directly dependent on the external data source.
NEW QUESTION # 278
How can you overwrite configurations for models within a package?
Choose 1 option.
Answer: A
Explanation:
The correct answer is B: By adding the configurations in dbt_project.yml within the package's namespace.
dbt packages are installed as read-only code, meaning you should never modify files inside /dbt_packages, nor should you attempt to overwrite generated artifacts inside /target. Instead, dbt provides a clean and officially supported method for overriding or extending configurations on models from packages: using the dbt_project.yml file.
Each package comes with its own namespace, and users can override model-level configurations by referencing that namespace inside the models: section of dbt_project.yml. dbt merges project-level configurations with package-level defaults, and configurations defined in dbt_project.yml take precedence.
This is the only correct and recommended way to override settings such as materializations, tags, schema, or custom config blocks on models imported through a package.
Option A is incorrect because packages.yml is used only to declare package dependencies-not to configure package models. Option C is incorrect because the ./target folder contains compiled and ephemeral artifacts that should never be edited. Option D is incorrect because modifying code in /dbt_packages violates dbt best practices and will be overwritten each time packages are reinstalled.
Therefore, overriding configurations must be done exclusively through dbt_project.yml.
NEW QUESTION # 279
32. You are creating a fct_tasks model with this CTE:
with tasks as (
select * from {{ ref('stg_tasks') }}
)
You receive this compilation error in dbt:
Compilation Error in model fct_tasks (models/marts/fct_tasks.sql)
Model 'model.dbt_project.fct_tasks' (models/marts/fct_tasks.sql) depends on a node named 'stg_tasks' which was not found Which is correct? Choose 1 option.
Options:
Answer: B
Explanation:
The dbt compilation error explicitly states that the model fct_tasks depends on a node named stg_tasks, but dbt cannot find any resource with that name in the project. dbt resolves ref('stg_tasks') at compile time by searching for a model, seed, or source named stg_tasks. When no such model exists, dbt raises the exact error shown: "depends on a node named 'stg_tasks' which was not found." This error occurs before execution and is unrelated to whether the table exists in the warehouse. For dbt, the existence of a warehouse table is irrelevant during compilation-only the presence of a declared dbt resource matters. Therefore, option C (no table in the warehouse) cannot cause this error.
Option A is incorrect because ephemeral models still count as dbt models, and dbt can resolve ref() to them without problem.
Option D is wrong because defining a model in schema.yml is optional and unrelated to dbt's ability to find the model. Tests and documentation require YAML entries, but model definitions do not.
Thus, the only correct explanation is that no model file named stg_tasks.sql exists under /models, making Option B the correct choice.
NEW QUESTION # 280
You're collaborating on a project where team members have varying levels of dbt experience. How can you use the dbt_project.yml to help standardize practices?
Answer: A
Explanation:
Project-level tests in dbt_project.yml provide a centralized way to maintain data quality expectations across the team.
NEW QUESTION # 281
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