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| Section | Objectives |
|---|---|
| Testing and Data Quality | - Built-in and custom tests
|
| Documentation and Lineage | - Data lineage understanding
|
| Deployment and Orchestration | - Environments and workflows
|
| Analytics Engineering Foundations | - Modern data stack concepts (ELT vs ETL)
|
| dbt Core Concepts | - Project structure and configuration
|
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NEW QUESTION # 304
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."
Answer: A
Explanation:
Git does not automatically reject or ignore changes from the second branch (feature_b). Instead, Git attempts to merge both sets of changes, and if they modify the same lines or nearby blocks of code, Git produces a merge conflict that must be manually resolved. In this scenario, both feature_a and feature_b introduce new columns into the same SELECT statement of the same model file, meaning Git must reconcile two different edits made in parallel on branches that diverged from the same commit.
Once feature_a is merged into main, the code in main contains the new column customer_country. When developer B then tries to merge feature_b, Git compares the modified file in feature_b with the updated file in main. Since both branches changed the same section of SQL, Git cannot automatically determine the correct combined output. It will not discard feature_b's changes; instead, Git requires the developer to manually merge both sets of additions, typically resulting in a combined SELECT clause with both customer_country and customer_address, unless the developer chooses otherwise.
This behavior is documented in Git fundamentals: when multiple developers modify the same file region, manual conflict resolution is required. Therefore, the statement claiming feature_b's changes "won't be added" is incorrect.
NEW QUESTION # 305
A downstream process relies on a critical model to be fully refreshed daily. How can you enforce this behavior using dbt_project.yml configuration?
Answer: B
Explanation:
Post-hooks enable you to run operations after a model executes. The other options don't provide the required control over the refresh behavior.
NEW QUESTION # 306
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.
Answer: E
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 # 307
A large dimension table is joined frequently in your dbt models. Due to its size, these joins significantly impact performance. Which strategy might offer improvement?
Answer: A
Explanation:
While other options could help marginally, they don't address the large-table join issue head- on. Database-level join optimizations are crucial for this scenario.
NEW QUESTION # 308
You're getting inconsistent errors when adding new models in your dbt_project.yml. You suspect typos or indentation issues. How can you proactively improve this situation?
Answer: B
Explanation:
Each of these plays a role in preventing YAML errors.
NEW QUESTION # 309
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