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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing and Documentation | 20% | - Custom data tests - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Documentation generation |
| Topic 2: Deployment and Orchestration | 15% | - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud - Environments (dev, staging, prod) - Git version control integration |
| Topic 3: Data Transformation Techniques | 25% | - Common table expressions and subqueries - Macros and packages - Jinja templating - Refactoring and incremental models |
| Topic 4: Models | 25% | - Materializations (table, view, ephemeral, incremental) - Writing and managing SQL models - Seeds - Sources and references - Snapshots |
| Topic 5: dbt Fundamentals | 15% | - dbt project structure - dbt Core vs dbt Cloud - dbt workflow and best practices |
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NEW QUESTION # 298
Which of the following is true about restricting the usage of models in dbt?
Choose 1 option.
Answer: A
Explanation:
The correct answer is C: Model groups can limit references by other models which aren't in the same group.
According to the dbt documentation, dbt provides a formal access-control mechanism for models through model groups and the access property. Models can be assigned to groups, and each model can be marked as public, protected, or private. The purpose of this system is to create clear boundaries within a project, making it possible to control which models are allowed to reference others. Specifically, dbt enforces rules where models in a group can restrict references from models outside that group, which helps maintain modularity and prevents accidental coupling of unrelated data layers. This is extremely valuable in large analytics engineering projects where teams manage different domains or data products.
Option A is incorrect because dbt does not use Git user groups or any form of identity-based access control for model usage. Option B is incorrect because dbt does not integrate with warehouse roles for controlling model references-access is handled strictly within the dbt project's metadata. Option D is incorrect because user emails or platform identities have no role in determining which models can reference others. dbt enforces usage rules only through its metadata-driven grouping and access configuration system.
NEW QUESTION # 299
You add a new seed file. Here's a snippet of your dbt_project.yml:YAML
Answer: D
Explanation:
column_types config applies to individual seeds, not within the general seeds section. The others might be errors in different scenarios but less likely in this case-
NEW QUESTION # 300
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: C
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 # 301
You're working with a development data warehouse meant to closely mirror production. To make code more portable between environments, you opt to use environment variables for connection details. What's the best way to manage these?
Answer: B,C
Explanation:
B provides the most secure and easily auditable solution. D is essential for automating deployments. A is insecure practice. C is cumbersome and error-prone.
NEW QUESTION # 302
You've meticulously added descriptions and tests to your dbt models. However, after running dbt docs generate, the tests don't appear in the generated documentation. Which of the following reasons are most likely?
Answer: B,C
Explanation:
A: Custom macros often need explicit configuration to be included in the generated documentation. C: The dbt_project.yml file controls which elements (models, tests, etc.) are included in the docs- You'll need to verify the relevant settings.
NEW QUESTION # 303
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