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Consider this DAG:
app_data.detail_categories -> stg_detail_categories -> skills_with_details app_data.details -> stg_details -> lessons_with_details What will support making this DAG more modular? Choose 1 option.
A. Union stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
B. Combine lessons_with_details and skills_with_details into one wide table called curriculum_with_details.
C. Consolidate the two staging models into one model and then use this downstream for both skills_with_details and curriculum_with_details.
D. Examine the SQL of lessons_with_details and skills_with_details to see if there is a candidate for an intermediate model.
E. Join stg_detail_categories and stg_details in the staging layer to reduce the need for downstream joins.
Answer: D
Explanation:
Modularity in dbt means breaking transformations into small, reusable steps that are easy to reason about, test, and reuse. In this DAG, two staging models (stg_detail_categories and stg_details) feed into two mart- level models (skills_with_details and lessons_with_details). If there is repeated logic in those two mart models-such as common joins, filters, or derived fields-that logic is a good candidate to be extracted into an intermediate model.
Option E follows dbt best practices: inspect the SQL of lessons_with_details and skills_with_details and identify shared logic to move into a new intermediate model (for example, int_details_with_categories). Both downstream models can then reference this intermediate model using ref(), centralizing logic and improving maintainability.
Options A and B push joins/unions into the staging layer, which should focus on clean, 1:1 representations of sources-not combining multiple sources. Option C collapses distinct staging models into one, reducing clarity and breaking the source-to-staging contract. Option D creates an overly wide, less reusable final table instead of focusing on shared reusable logic.
Therefore, the action that truly increases modularity is Option E: creating an intermediate model based on common SQL patterns.
NEW QUESTION # 140
You're using dbt Cloud and want to tighten control over when production deployments can occur. What dbt Cloud feature, in conjunction with your CI/CD configuration, would allow you to enforce this?
A. dbt Cloud's built-in source freshness checks.
B. Deployment jobs and schedules.
C. Environment variables for sensitive credentials.
D. Webhooks for triggering jobs by external events.
Answer: B
Explanation:
B enables you to define specific times or conditions for production deployments. Others are less directly related to controlling the timing.
NEW QUESTION # 141
You want to standardize descriptions across a large number of models that share the same underlying source schemas. Which approach might be the most efficient?
A. Focus on using a version control system and enforce descriptions directly within the source system.
B. Create a CSV lookup file and maintain custom source descriptions within the file.
C. Write a Jinja macro that dynamically generates descriptions based on metadata.
D. Manually copy and paste the descriptions into each model's YAML file.
Answer: C
Explanation:
Jinja macros offer flexibility and automation, making them ideal for generating consistent descriptions across many models based on source metadata
NEW QUESTION # 142
You need a model to track weekly cohort retention, with the ability to slice data across multiple dimensions. The underlying data is voluminous. Which approach lays a good foundation for scalability?
A. Introduce a JSON column to store dimensions dynamically, reducing the number of columns.
B. Normalize the cohort data and dimensions, using joins for analysis.
C. Pre-calculate all possible cohort and dimension combinations as an incremental model.
D. A wide table with columns for each potential filtering combination (country, product category, acquisition channel, etc.).
Answer: B
Explanation:
Normalization aids scalability. Options A and C suffer from combinatorial explosion. While JSON can offer flexibility, query performance can be unpredictable.
NEW QUESTION # 143
You need to test whether the values of a specific column have changed over time. What might be the best approach to implement this kind of test within your dbt project?
A. Use a custom 'accepted_values' test together with a dbt seed.
B. Write a dbt macro to create a temporary table with historical data to compare against.
C. rely on native version history features in your data warehouse (if available).
D. Use a snapshot to preserve a point-in-time reference of the column.
Answer: B,C,D
Explanation:
A provides a clean comparison point C enables more complex historical analysis. D leverages built-in warehouse features, if you have them.
NEW QUESTION # 144
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