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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Topic 2: Developing dbt Models | 20% | - Model optimization
|
| Topic 3: Implementing dbt Tests | 10% | - Custom tests - Built-in tests - Test configuration and execution |
| Topic 4: Creating and Maintaining Documentation | 10% | - Generating documentation - Documentation standards - Descriptions and metadata |
| Topic 5: dbt Models Governance | 15% | - Version control integration - Naming conventions and standards - Project organization and structure |
| Topic 6: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 7: Managing Data Pipelines | 15% | - CI/CD integration - Pipeline orchestration - Deployment strategies |
| Topic 8: Debugging and Error Resolution | 15% | - Resolving data quality issues - Identifying modeling errors - Debugging techniques |
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NEW QUESTION # 213
(Multiple Select)
Answer: B,C,D
Explanation:
A enables environment-specific commands. B provides a way to launch different jobs. C handles scheduled tasks within dbt Cloud.
NEW QUESTION # 214
You are working on a complex dbt model with many Common Table Expressions (CTEs) and decide to move some of those CTEs into their own model to make your code more modular.
Is this a benefit of this approach?
The new model can be documented to explain its purpose and the logic it contains.
Answer: B
Explanation:
Yes, this is a benefit of breaking large CTE-heavy SQL models into modular dbt models. According to dbt and Analytics Engineering best practices, modularity improves clarity, maintainability, and documentation quality. When CTEs remain embedded inside a single large SQL file, their purposes are often unclear, difficult to document, and hard for other developers to reuse. By extracting a logical CTE into its own model, dbt treats it as a first-class resource-meaning it can have its own description, tests, documentation, lineage, and metadata defined in YAML.
dbt's documentation system allows each model to include a description explaining what the transformation does, the assumptions being made, and the expected behavior of the data. This aligns with the Analytics Engineering principle of creating self-documenting pipelines, where transformations are transparent and easier for downstream users to understand.
Additionally, modular models improve lineage visualization in the DAG. Instead of a single model hiding multiple transformation layers, a modular structure reveals how data flows through each intermediate step, helping both debugging and governance. Modularization also enables reusability-other models can reference the intermediate model rather than rebuilding the same logic through duplicated CTEs, supporting DRY (Don't Repeat Yourself) principles.
Therefore, moving CTEs into separate dbt models absolutely provides a documentation benefit and improves the overall engineering quality of the project.
NEW QUESTION # 215
You create tests to validate counts and timestamps against a baseline dataset. The tests fail unexpectedly. What might be possible root causes?
Answer: A,B,C,D
Explanation:
Each scenario could cause a baseline comparison test to fail, covering issues in the data pipeline, the baseline, and the test logic itself.
NEW QUESTION # 216
Consider these SQL and YAML files for the model model_a:
models/staging/model_a.sql
{{ config(
materialized = "view"
) }}
with customers as (
...
)
dbt_project.yml
models:
my_new_project:
+materialized: table
staging:
+materialized: ephemeral
Which is true about model_a? Choose 1 option.
Options:
Answer: C
Explanation:
model_a contains an in-model config explicitly setting:
{{ config(materialized = "view") }}
In dbt, in-model config overrides project-level config, including folder-level defaults. Even though the staging/ folder is configured as:
staging:
+materialized: ephemeral
...this does not apply because the in-file config has higher precedence.
So model_a is materialized as a view.
A view in dbt is not persisted as data; instead, it stores the SQL definition. When a downstream query selects from that view, the database executes the underlying SQL at query time. As a result:
* Queries run slower than selecting from a table, because the underlying computation happens each time.
* But the data is always up to date, because views read directly from the current underlying tables.
This matches Option C.
Why the other options are wrong
* A & D describe table behavior (faster queries, data only as fresh as last run). model_a is not materialized as a table.
* B is incorrect because views are fully queryable; no error occurs.
NEW QUESTION # 217
You want to configure dbt to prevent tests from running if one or more of their parent models is unselected.
Which test command should you execute?
Choose 1 option.
Answer: A
NEW QUESTION # 218
......
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