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| Section | Weight | Objectives |
|---|---|---|
| Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Generating documentation - Documentation standards |
| Developing dbt Models | 20% | - Model design and structure
|
| dbt Models Governance | 15% | - Naming conventions and standards - Version control integration - Project organization and structure |
| Implementing dbt Tests | 10% | - Test configuration and execution - Built-in tests - Custom tests |
| Managing Data Pipelines | 15% | - Deployment strategies - CI/CD integration - Pipeline orchestration |
| External Dependencies | 10% | - Using packages - External sources integration - Managing snapshots |
| Leveraging dbt State | 5% | - State-aware operations - State management |
| Debugging and Error Resolution | 15% | - Resolving data quality issues - Identifying modeling errors - Debugging techniques |
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NEW QUESTION # 335
A complex transformation in your dbt project pulls data from an external API. Due to rate limits, this API endpoint cannot be queried frequently in production. How could you handle development and testing of this transformation effectively?
Answer: D
Explanation:
A: Mocks enable testing without impacting the production API- B: Environment variables allow for flexible configuration. C: Snapshots provide static samples for testing in isolation.
NEW QUESTION # 336
You're working with a model that aggregates data across many countries. Which assumption about the data might be especially important to test for and handle in your model logic?
Answer: A,B,D
Explanation:
Each of these introduces unique challenges for aggregating data globally. While D can be an issue, it's less central to this specific scenario.
NEW QUESTION # 337
The results of a dbt model contain values outside the expected range. You suspect an issue with Jinja templating. Where might this issue originate?
Answer: B
Explanation:
Jinja templating can exist in your project configuration files, macros, and the model SQL itself.
NEW QUESTION # 338
Which two mechanisms allow dbt to write DRY code by reusing logic, preventing writing the same code multiple times?
Choose 2 options.
Answer: B,C
Explanation:
The correct answers are B: writing and using dbt macros and D: using dbt packages.
dbt strongly encourages DRY (Don't Repeat Yourself) principles, and two of the core mechanisms that support reusable logic are macros and packages. Macros allow you to write Jinja-powered reusable functions that can generate SQL statements dynamically, reducing duplication across models, tests, and project logic.
Macros can encapsulate filters, joins, auditing logic, timestamps, and more-allowing developers to centralize logic in one place while referencing it across many models.
Packages extend this concept even further by allowing entire sets of macros, models, tests, and utilities to be imported into a project. Packages like dbt-utils contain widely used generic macros that help standardize transformations and testing. Using packages ensures consistent logic across teams and eliminates the need to rewrite common transformations.
Option A contradicts DRY principles because copy/pasting increases maintenance burden. Option C is not a mechanism for reusing logic; singular tests validate logic but do not reduce duplication. Option E simply changes a model's materialization and does not support code reuse.
Thus, macros and packages are the only correct dbt mechanisms that provide reusable, modular, DRY logic.
NEW QUESTION # 339
A teammate opens a pull request with numerous changes across many files. To help with the review process, you would suggest that they:
Answer: C
Explanation:
A: Smaller PRs allow for more focused and effective reviews. B: Clear commit messages help understand the step-by-step changes within the PR. C: A summary provides a bird's-eye view for the reviewer.
NEW QUESTION # 340
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