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| Section | Objectives |
|---|---|
| Deployment and Orchestration | - Environments and workflows
|
| Documentation and Lineage | - Data lineage understanding
|
| Testing and Data Quality | - Data validation strategies
|
| Analytics Engineering Foundations | - Modern data stack concepts (ELT vs ETL)
|
| dbt Core Concepts | - Models and materializations
|
>> New dbt-Analytics-Engineering Test Sample <<
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NEW QUESTION # 22
Your dbt_project.yml contains the following: YAML
Answer: D
Explanation:
YAML relies on precise indentation. Materialization should be at the same level as the model name, not nested further.
NEW QUESTION # 23
You've created a new model ref_country_codes. It references a raw table in your data warehouse raw_web_events. To ensure this dependency is valid and up-to-date, you should include which element in your model definition?
Answer: C
Explanation:
Verifying raw object dependencies is about data freshness. A source freshness test on the upstream source raw_web_events will alert you if that table becomes stale, causing potential issues in your ref_country_codes model.
NEW QUESTION # 24 
Answer:
Explanation:
Explanation:
(new_project)
(models:)
(grants)
(require-dbt-version: '>= 1.0.0' )
The dbt_project.yml file acts as the configuration backbone for every dbt project. dbt requires that project names follow a strict naming convention, allowing only lowercase letters, numbers, and underscores.
Therefore, new_project is the only valid name among the choices. Names with spaces or hyphens, such as new project or new-project, violate dbt's project naming rules.
To configure how models behave-materializations, schemas, tags, or per-directory overrides-dbt uses the models: block inside dbt_project.yml. This block defines configuration inheritance rules and is different from the config() function, which is used inside SQL models, not in YAML files.
When granting permissions on database objects created by dbt, you use the grants configuration. This allows you to define access roles at the model level or project level for downstream BI or analytics tools, ensuring proper data governance and controlled data exposure.
Finally, compatibility with specific dbt versions is set using require-dbt-version. The correct YAML formatting uses quotes, such as '>= 1.0.0', ensuring dbt interprets the value as a string. This prevents accidental mis-parsing and enforces version requirements during dbt run or dbt build.
NEW QUESTION # 25
You've made several commits to your feature branch. Before merging into the main branch, you want to consolidate the changes into a single, clear commit. What Git technique would help achieve this?
Answer: D
Explanation:
Interactive rebasing (git rebase -i) provides fine-grained control to squash, edit, or rearrange commits, creating a cleaner commit history
NEW QUESTION # 26
You need to test for consistency between a raw source table and its corresponding dbt model after transformation. This requires comparing many columns for null counts and matching value distributions. Which approach would be most efficient?
Answer: B
Explanation:
A is tedious, B is too coarse-grained, and D is insufficient. A custom test designed for this comparison provides automation and the needed specificity.
NEW QUESTION # 27
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