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| Section | Weight | Objectives |
|---|---|---|
| dbt Fundamentals | 15% | - dbt Core vs dbt Cloud - dbt workflow and best practices - dbt project structure |
| Testing and Documentation | 20% | - Schema tests (unique, not_null, accepted_values, relationships) - dbt docs and DAG visualization - Custom data tests - Documentation generation |
| Data Transformation Techniques | 25% | - Macros and packages - Jinja templating - Refactoring and incremental models - Common table expressions and subqueries |
| Deployment and Orchestration | 15% | - Jobs and scheduling in dbt Cloud - Git version control integration - Environments (dev, staging, prod) - CI/CD with dbt Cloud |
| Models | 25% | - Materializations (table, view, ephemeral, incremental) - Snapshots - Sources and references - Seeds - Writing and managing SQL models |
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NEW QUESTION # 80
You've created a complex lineage macro, and you want to include unit tests for the macro logic itself. How might you approach this?
Answer: C
Explanation:
While dbt doesn't have a dedicated macro testing framework, Jinja provides ways to construct tests. However, there's no built-in mechanism to run these as part of your typical dbt workflow.
NEW QUESTION # 81
You introduce a new test, which unexpectedly leads to many models previously deemed 'successful' now failing.
What should your initial troubleshooting steps include?
Answer: A,B,C
Explanation:
The focus is to determine if the issue is with the test itself, the data, or how the test is interpreted within your workflow. D is a last resort that risks masking real issues.
NEW QUESTION # 82
You are working with git to version control the dbt logic.
Order these steps to add or modify transformations to your dbt project.
Answer:
Explanation:
* Create a new branch in git and switch to itB. Update the dbt code according to the new logic
C. Create a Pull / Merge RequestD. Run some automated CI checks and/or manual review of the updated codeE. Merge the updated code to the main git branch
* Version control best practices in dbt follow the same engineering workflow used in modern software development. The first step is always to create a new git branch and switch into it, ensuring all work is isolated from production-ready code and allowing your team to develop safely without affecting others.
Once inside the feature branch, you update the dbt code according to the new logic, which may include modifying models, tests, macros, or documentation.
* Next, you create a Pull Request (PR), also known as a Merge Request, to propose integrating your changes into the main branch. This is important because dbt projects are collaborative, and PRs facilitate peer review, enforce project standards, and prevent regressions. Once the PR is created, automated CI pipelines-such as running dbt build, schema tests, data quality checks, and code-style checks-are executed. Reviewers may also manually inspect code for logic correctness, naming conventions, and modeling consistency.
* After all checks have passed and reviewers approve the PR, the final step is to merge the updated code into the main branch, making the new transformations part of the production dbt project. This workflow ensures governance, reliability, and auditable development practices, all of which are core principles in analytics engineering.
NEW QUESTION # 83
You frequently need to spin up short-lived development environments on demand for testing and experimentation. Which dbt feature would streamline this process, especially when it involves custom configuration?
Answer: A,B
Explanation:
A encapsulates environment settings in reusable blocks- B allows for ad-hoc adjustments when running dbt- C is relevant for cloud-based setups, but less flexible for on-the-fly changes- D relates to model selection, not environment setup.
NEW QUESTION # 84
You have created a model called stg_tasks and now you need to implement tests.
You provide this in schema.yml:
version: 2
models:
- name: stg_tasks
columns:
- name: completed_at
tests:
- not_null:
- config:
where: "state = 'completed'"
You receive this compilation error:
[WARNING]: Did not find matching node for patch with name 'stg_tasks' in the 'models' section of file
'models/example/schema.yml'
How can you change the configuration on the not_null test to fix this compiler error?
Answer: C
Explanation:
In dbt, when you configure a generic test like not_null in YAML, the configuration for that test must be a mapping, not another list item. The correct structure is:
tests:
- not_null:
config:
where: "state = 'completed'"
In your original YAML, you wrote:
tests:
- not_null:
- config:
where: "state = 'completed'"
The extra dash (- config) makes config an element of a list rather than a key under the not_null test. This breaks the expected shape of the test definition. When dbt parses the schema.yml, it fails to correctly interpret the patch for the stg_tasks model, which leads to the warning: "Did not find matching node for patch with name 'stg_tasks'...".
By removing the extra dash and nesting config directly under not_null, dbt now reads this as a single generic test named not_null with a config block that passes the where argument. This allows dbt to correctly attach the test to the completed_at column of the stg_tasks model and eliminates the compiler warning.
Therefore, Option B is the only structurally valid configuration and is the correct answer.
NEW QUESTION # 85
......
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