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| Section | Objectives |
|---|---|
| Documentation and Lineage | - Data lineage understanding
|
| Testing and Data Quality | - Built-in and custom tests
|
| Deployment and Orchestration | - Environments and workflows
|
| dbt Core Concepts | - Project structure and configuration
|
| Analytics Engineering Foundations | - SQL proficiency for analytics
|
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NEW QUESTION # 52 
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 # 53
You want to run and test the models, tests, and snapshots you have added or modified in development.
Which will you invoke? Choose 1 option.
Options:
Answer: E
Explanation:
The goal in this scenario is:
* Select only models, tests, and snapshots that were modified in development, using:state:modified
* Use already-built production objects whenever possible, so development does not need to rebuild everything. This is done with:--defer
* Compare development code against production artifacts, which requires:--state <path/to/artifacts>
* Run everything (models + tests + snapshots) in a single command. This is exactly what dbt build does.
* dbt build = run models + run tests + run snapshots + run seeds.
Putting it all together, the correct command is:
dbt build --select state:modified --defer --state <path/to/artifacts>
This single command satisfies all requirements:
* It runs only modified resources.
* It uses the production state artifacts for comparison.
* It defers to production-built models where possible.
* It runs all resource types automatically.
Why the other options are incorrect:
* A # Missing --state, so state:modified cannot function.
* B # Splits run + test separately; question asks which single command you will invoke.
* D # Missing --defer, which defeats the purpose of using production objects.
* E # Also missing --defer.
Thus, Option C is the only correct answer.
NEW QUESTION # 54
A sudden data quality issue occurs in production. You need to quickly reproduce the problem in a lower environment but retain a point-in-time snapshot of production dat a. Which strategies might you employ? Create a backup of the production database and restore it to a separate development or testing database.
Answer: C
Explanation:
A: Backups offer static point-in-time snapshots. B: Replication might be ideal for immediate mirroring, if available. C: Snapshots are lightweight yet can be time-sensitive.
NEW QUESTION # 55
You're working in a large, enterprise-level dbt deployment. Which aspect of using dbt documentation commands could become especially important in this context?
Answer: A
Explanation:
All aspects become increasingly important in larger projects:A: Optimization matters when many models create more complex documentation. B: Tracking documentation history alongside code is crucial for governance. C: Clear guidelines are essential with many contributors.
NEW QUESTION # 56
You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?
Answer: A,D
Explanation:
B is relevant if you use dbt Cloud. C is suitable, as many observability platforms have features for tracking operational data, including dbt job metrics. A and D, while containing information, are likely too granular or require a lot of manual work.
NEW QUESTION # 57
......
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