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| Section | Weight | Objectives |
|---|---|---|
| Managing Data Pipelines | 15% | - Pipeline orchestration - CI/CD integration - Deployment strategies |
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Debugging techniques - Identifying modeling errors |
| Implementing dbt Tests | 10% | - Test configuration and execution - Custom tests - Built-in tests |
| External Dependencies | 10% | - External sources integration - Managing snapshots - Using packages |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| dbt Models Governance | 15% | - Version control integration - Naming conventions and standards - Project organization and structure |
| Developing dbt Models | 20% | - Model design and structure
|
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Documentation standards - Generating documentation |
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질문 # 290
Outdated Macro
정답：A,D
설명：
Unit tests isolate the macro's logic, while a staging environment allows for real-world impact assessment. A separate source table might be overkill; dbt Is focuses on dependencies, not functionality.
질문 # 291
You want to include a glossary of terms in your dbt project docs so that users can easily reference definitions. How could you achieve this?
정답：A,B
설명：
A: A flexible approach with Jinja macros and a glossary file allows you to dynamically include your glossary in the documentation_ D. Custom macros can provide automation by extracting glossary terms from specifically marked column descriptions.
질문 # 292
You're onboarding new developers to your dbt project. To simplify their setup, you want to provide a standard profiles.yml template. What should you consider when doing this?
정답：A,B,D
설명：
B promotes flexibility and security (avoids storing sensitive data). C demonstrates good practices. D enhances usability A is a major security risk!
질문 # 293
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."
정답：A
설명：
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.
질문 # 294
You're setting up baseline comparisons with historical dat
a. Which factors are crucial to consider when selecting or generating the baseline dataset?
정답：A,C,D
설명：
Each of these directly impacts the validity and usefulness of the baseline for comparison. B, while good, is less universally critical.
질문 # 295
......
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