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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: External Dependencies | 10% | - Using packages - Managing snapshots - External sources integration |
| Topic 2: Managing Data Pipelines | 15% | - Deployment strategies - Pipeline orchestration - CI/CD integration |
| Topic 3: Implementing dbt Tests | 10% | - Test configuration and execution - Built-in tests - Custom tests |
| Topic 4: Creating and Maintaining Documentation | 10% | - Generating documentation - Descriptions and metadata - Documentation standards |
| Topic 5: Debugging and Error Resolution | 15% | - Resolving data quality issues - Debugging techniques - Identifying modeling errors |
| Topic 6: dbt Models Governance | 15% | - Version control integration - Naming conventions and standards - Project organization and structure |
| Topic 7: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 8: Developing dbt Models | 20% | - Model optimization
|
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NEW QUESTION # 213
You are working on a complex dbt model with many Common Table Expressions (CTEs) and decide to move some of those CTEs into their own model to make your code more modular.
Is this a benefit of this approach?
The new model can be documented to explain its purpose and the logic it contains.
Answer: B
Explanation:
Yes, this is a benefit of breaking large CTE-heavy SQL models into modular dbt models. According to dbt and Analytics Engineering best practices, modularity improves clarity, maintainability, and documentation quality. When CTEs remain embedded inside a single large SQL file, their purposes are often unclear, difficult to document, and hard for other developers to reuse. By extracting a logical CTE into its own model, dbt treats it as a first-class resource-meaning it can have its own description, tests, documentation, lineage, and metadata defined in YAML.
dbt's documentation system allows each model to include a description explaining what the transformation does, the assumptions being made, and the expected behavior of the data. This aligns with the Analytics Engineering principle of creating self-documenting pipelines, where transformations are transparent and easier for downstream users to understand.
Additionally, modular models improve lineage visualization in the DAG. Instead of a single model hiding multiple transformation layers, a modular structure reveals how data flows through each intermediate step, helping both debugging and governance. Modularization also enables reusability-other models can reference the intermediate model rather than rebuilding the same logic through duplicated CTEs, supporting DRY (Don't Repeat Yourself) principles.
Therefore, moving CTEs into separate dbt models absolutely provides a documentation benefit and improves the overall engineering quality of the project.
NEW QUESTION # 214
A team member has opened a pull request, but conflicts arise when you attempt to merge it. Which of the following are essential steps in the conflict resolution process?
Answer: B
Explanation:
A: You'll need to carefully edit files to combine the changes correctly. B: Git conflict markers visually highlight where conflicts need to be addressed. C: Communication is key to determining the best way to reconcile conflicting changes and maintain code intent.
NEW QUESTION # 215
You encounter a dbt model using a series of nested subqueries to achieve complex filtering. Performance is poor. Which common alternative is likely to provide a performance boost?
Answer: C
Explanation:
Databases typically optimize joins more effectively than deeply nested subqueries. The other options don't directly address the core performance issue.
NEW QUESTION # 216
"Our dbt jobs take hours to run, and it's hard to know why!" says a frustrated colleague. What initial investigation steps would you suggest?
Answer: B
Explanation:
Each of these could provide valuable insights. Start with profiling to find the most performance-critical areas, but don't neglect hooks and potential config-level optimizations.
NEW QUESTION # 217
You run this command:
dbt build --select "source_status:fresher+" --state path/to/prod/artifacts Which two need to happen before it can be executed successfully?
Choose 2 options.
Answer: C,E
Explanation:
The selector source_status:fresher+ depends entirely on freshness metadata created by a previous dbt source freshness run. Source freshness results are stored in artifacts, and the --state flag points dbt to the location where these artifacts were previously generated. Without first running dbt source freshness, dbt has no stored freshness status and therefore cannot filter sources by "fresher" status. This makes option A required.
Additionally, source freshness checks only work when sources include a freshness block in their YAML configuration. This block specifies the loaded_at_field and the warn_after / error_after thresholds. If no freshness block exists, dbt does not compute freshness at all, meaning such sources cannot appear in the source_status:fresher+ selector. Therefore D is also required.
Options B and E are unrelated to source freshness artifacts and do not contribute to the preconditions for this selector. Option C does not impact freshness logic, as generic tests have no role in generating freshness metadata.
Thus, the two prerequisites are:
* Running dbt source freshness (to create freshness metadata).
* Defining a freshness configuration on the relevant sources.
NEW QUESTION # 218
......
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