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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: External Dependencies | 10% | - External sources integration - Using packages - Managing snapshots |
| Topic 2: Creating and Maintaining Documentation | 10% | - Generating documentation - Documentation standards - Descriptions and metadata |
| Topic 3: Implementing dbt Tests | 10% | - Built-in tests - Custom tests - Test configuration and execution |
| Topic 4: Debugging and Error Resolution | 15% | - Debugging techniques - Resolving data quality issues - Identifying modeling errors |
| Topic 5: Managing Data Pipelines | 15% | - CI/CD integration - Pipeline orchestration - Deployment strategies |
| Topic 6: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 7: Developing dbt Models | 20% | - Model optimization
|
| Topic 8: dbt Models Governance | 15% | - Version control integration - Naming conventions and standards - Project organization and structure |
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NEW QUESTION # 165
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: B
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 # 166
During development, you frequently need to refresh and transform large datasets, leading to long dbt run times. Which techniques could you explore to improve your development workflow?
Answer: D
Explanation:
A: Incremental models optimize execution when changes are isolated. B: Ephemeral models avoid unnecessary persistence for temporary steps. C: Sampling decreases data volumes, speeding up development runs.
NEW QUESTION # 167
You need to override a source description at the model level. How would you achieve this most effectively?
Answer: B
Explanation:
The meta configuration can be used to override source descriptions and other metadata at the model level. However, make sure to check the documentation for your specific dbt version, as the exact way to do this might vary_
NEW QUESTION # 168
You discover that dbt models run significantly slower in your production environment than in development. What potential causes should you investigate?
Answer: A,B,D
Explanation:
Each of these directly affects performance and could differ significantly between environments. C is relevant, but less likely the sole cause if development is performant.
NEW QUESTION # 169
Several of your users report that dashboards built on top of your dbt models are intermittently displaying stale dat a. Which of the following might be a potential cause?
Answer: D
Explanation:
Each of these could lead to stale data Start by examining freshness checks, then materializations, and if those are correct, inspect the DAG for execution bottlenecks.
NEW QUESTION # 170
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