Dependency Break

• A.Adding schema tests to the source table to catch structural changes early on.
• B.Using the dbt deps command to visualize dependencies and anticipate potential impacts.
• C.Setting up alerts or notifications if dbt runs fail unexpectedly in a production environment.
• D.All of the above.

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You inherit a project with a monolithic "super-model" performing many transformations. Why might refactoring this into smaller, interconnected models be beneficial?

• A.Reduces the risk of a single point of failure impacting the entire data flow.
• B.Enables faster debugging and troubleshooting by isolating issues.
• C.Increases potential for parallel execution within your data warehouse.
• D.All of the above.

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You want to trigger a dbt job immediately upon successful completion of an upstream ETL process. Which methods could achieve this?

• A.Configuring the ETL process to send a completion notification to your CIICD tool, triggering the dbt job.
• B.Using dbt Cloud's webhooks to create an endpoint that your ETL process can call.
• C.Scheduling a dbt job with a very frequent interval (e.g., every 5 minutes) to poll for changes in a metadata table.
• D.Using the dbt build command to run all dependent models after a successful dbt run.

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Examine model stg_customers_sales that exists in the main branch:
select
id as customer_id,
name as customer_name
from {{ source('my_data','my_source') }}
A developer creates a branch feature_a from main and modifies the model as:
select
id as customer_id,
name as customer_name,
country as customer_country
from {{ source('my_data','my_source') }}
A second developer also creates a branch feature_b from main and modifies the model as:
select
id as customer_id,
name as customer_name,
address as customer_address
from {{ source('my_data','my_source') }}
The first developer creates a PR and merges feature_a into main.
Then the second developer creates a PR and attempts to merge feature_b into main.
How will git combine the code from feature_b and the code from main, which now contains the changes from feature_a as well?
Statement:
"As feature_a is already approved and merged to main, the code for the model stg_customers_sales will stay as-is and the changes from feature_b won't be added."

• A.Yes
• B.No

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A dbt run failed with an error message.
Order these steps to fix your pipeline.

Correct Answer:

Explanation:
Check your terminal or log file for the output from the most recent dbt run.
Look for specific error messages associated with the models that failed.
3## Isolate the problem by dbt run --select model_name to run a single model and confirm whether the issue is localized to that model.
4## Use dbt run --select model_name+ to run the model and its downstream dependencies, ensuring that your fix works across the DAG.
Brief Explanation
* First, you always inspect the latest run output (step 1).
* Then, identify the exact failing models and error messages (step 2).
* Next, you reproduce the issue on the individual model to be sure the fix works locally (step 3).
* Finally, you re-run the model plus its downstream dependencies to validate the fix across the DAG (step 4).

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