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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 2: dbt Models Governance | 15% | - Version control integration - Naming conventions and standards - Project organization and structure |
| Topic 3: Developing dbt Models | 20% | - Model optimization
|
| Topic 4: Implementing dbt Tests | 10% | - Test configuration and execution - Custom tests - Built-in tests |
| Topic 5: Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors |
| Topic 6: Managing Data Pipelines | 15% | - CI/CD integration - Pipeline orchestration - Deployment strategies |
| Topic 7: Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Generating documentation - Documentation standards |
| Topic 8: External Dependencies | 10% | - Using packages - Managing snapshots - External sources integration |
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NEW QUESTION # 161
Which command materializes my_model and only its first-degree parent model(s) in the data platform?
Choose 1 option.
Answer: B
Explanation:
The correct answer is D: dbt run --select +1 my_model.
In dbt's selection syntax, the + operator is used to include parents (upstream) and/or children (downstream) of a given node. When used as +my_model or my_model+, it means "include all ancestors" or "all descendants" respectively, with no limit on depth.
To limit how many levels of parents or children are included, dbt allows a numeric depth between the + and the resource name. +1 my_model (or my_model+1) means:
* Select my_model
* Plus only its first-degree parents (direct dependencies), and no further ancestors.
Because you want to materialize the models, you must use dbt run, not dbt compile. dbt compile only compiles SQL and does not create or update relations in the data platform.
So:
* Option A (dbt run --select +my_model) would include all upstream ancestors, not just first-degree parents.
* Options B and E use compile, so they don't materialize.
* Option C is just syntactically wrong.
Therefore, the only command that both runs models and limits selection to my_model plus its first-degree parents is dbt run --select +1 my_model.
NEW QUESTION # 162
dbt Model Snippet:SQL
Answer: D
Explanation:
Subqueries can be performance bottlenecks- The other parts, while potentially optimizable, are less likely to cause a dramatic slowdown.
NEW QUESTION # 163
You notice that long descriptions within sources.yml cause formatting issues when viewing the docs, making them hard to read. What strategies could you employ to mitigate this?
Answer: A,B,D
Explanation:
B: Markdown syntax can improve long description readability within the generated docs. C: Third-party packages may offer customized formatting for source descriptions. D: Creating separate properties can provide an alternative way to manage both short and longer versions of a description.
NEW QUESTION # 164
Which two are true for a dbt clone command?
Choose 2 options.
Answer: C,D
Explanation:
The correct answers are A and C.
dbt clone is a command introduced to efficiently replicate existing relations (tables/views) from one schema
/environment to another within the same warehouse without fully rebuilding them. It typically leverages warehouse-level "clone" functionality (e.g., Snowflake zero-copy clone). To know what to clone, dbt needs information about previously built models; this is stored in artifacts like manifest.json. Therefore, A is correct:
it requires a reference to a previous manifest/state to know which models and locations to operate on.
C is also correct because cloning a production schema into a development schema enables you to test code changes while pointing downstream tools (like BI dashboards) at the cloned data. That way, BI or ML systems can exercise new dbt logic without impacting real production objects.
Option B describes state:modified / state comparison behavior, but dbt clone does create (or clone) objects, so B is false.
Option D is incorrect because clone works within a warehouse, not across different warehouses.
Option E is wrong: _v# suffixing is for model versioning, not cloning.
Thus, A and C correctly describe the behavior and purpose of dbt clone.
NEW QUESTION # 165
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: D
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 # 166
......
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