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In development, you want to avoid having to re-run all upstream models when refactoring part of your project.
What could you do to save time rebuilding models without spending warehouse credits in your next command?
A. Refer to a manifest and utilize the --defer and --state flags.
B. Leverage artifacts from a prior invocation by passing only the --state flag.
C. Clone your upstream models from the production schema to the development schema.
D. Replace your {{ ref() }} functions with hard-coded references.
Answer: A
Explanation:
The correct answer is B: Refer to a manifest and utilize the --defer and --state flags.
According to dbt's official documentation, the --defer flag enables developers to defer the resolution of model references (ref(), source()) to an existing manifest-commonly the one generated from a production run.
When this flag is used along with --state, dbt reads the prior project state and uses the already-built production models instead of rebuilding upstream dependencies. This is essential because production models are typically large and compute-intensive. By deferring to production artifacts, developers can test only the models they have modified, dramatically reducing warehouse costs and speeding up development cycles.
This approach is foundational to dbt's recommended development workflow, especially when working with CI
/CD or large DAGs. It preserves the integrity of the dependency graph while avoiding unnecessary recomputation.
Option A violates dbt best practices by removing dependency awareness. Option C is unnecessary operational overhead and does not integrate with dbt's state management. Option D is incomplete because --state alone does not prevent upstream model execution---defer is required to substitute those models with existing artifacts.
NEW QUESTION # 358
You've implemented the built-in dbt.get_column_values macro to display sample data values from a source column directly on the DAG. However, you notice performance issues when loading the docs. Which optimization strategy could you consider?
A. Replace dbt_get_column_values with a custom macro that only fetches the first few sample values.
B. Utilize a dbt snapshot to materialize the macro results instead of computing them on the fly.
C. Increase the compute resources allocated to your dbt documentation generation process.
D. Modify the macro to only retrieve sample values during development and not for production documentation.
Answer: A,B,D
Explanation:
B: Disabling expensive computations conditionally can boost performance for production environments. C: Snapshots pre-compute values, potentially improving DAG loading times. D: Reducing the amount of data retrieved by the macro could help with performance.
NEW QUESTION # 359
Your dbt project is part of a larger application where code for multiple components is merged into a shared main branch. Conflicts are becoming frequent. Which strategies could help mitigate merge conflicts?
A. Encourage frequent merging of smaller feature branches into the main branch.
B. Develop a communication process to notify teammates when changes are made to potentially shared models or sources.
C. All of the above.
D. Establish clear ownership and responsibilities for different areas of the dbt project.
Answer: C
Explanation:
Explanation: A Clear ownership reduces the likelihood of conflicting edits. B: Proactive communication helps avoid surprise changes that can cause conflicts_ C: Smaller changesets decrease the potential for conflicting changes across branches.
NEW QUESTION # 360
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:
A. Select statements made from the database on top of model_a will be quicker, but the data will only be as up to date as the last dbt run.
(Note: A and D are duplicates - typical exam formatting.)
B. Select statements made from the database on top of model_a will be slower, but the data will always be up to date.
C. Select statements made from the database on top of model_a will result in an error.
D. Select statements made from the database on top of model_a and transformation processing within model_a will be quicker, but the data will only be as up to date as the last dbt run.
Answer: B
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 # 361
You've just added some schema tests to a critical model. Which command should you execute to ensure these new tests are run?
A. dbt compile
B. dbt seed
C. dbt test
D. dbt run
Answer: C
Explanation:
The dbt test command is specifically designed to execute the tests you've defined within your models.
NEW QUESTION # 362
......
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