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| Section | Weight | Objectives |
|---|---|---|
| Leveraging dbt State | 5% | - State management - State-aware operations |
| Implementing dbt Tests | 10% | - Custom tests - Test configuration and execution - Built-in tests |
| dbt Models Governance | 15% | - Project organization and structure - Version control integration - Naming conventions and standards |
| External Dependencies | 10% | - Using packages - Managing snapshots - External sources integration |
| Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
| Developing dbt Models | 20% | - Model design and structure
|
| Debugging and Error Resolution | 15% | - Identifying modeling errors - Debugging techniques - Resolving data quality issues |
| Creating and Maintaining Documentation | 10% | - Documentation standards - Descriptions and metadata - Generating documentation |
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NEW QUESTION # 267
Your dbt project contains both source descriptions (within your sources.yml file) and model descriptions. You notice that the source descriptions don't appear in the generated docs alongside the relevant models. What's then most likely reason?
Answer: C
Explanation:
The dbt_project.yml file has settings that directly control the inclusion of source descriptions in the generated documentation. Its likely that you need to adjust these settings to display them correctly.
NEW QUESTION # 268
(Multiple Select)
Answer: A,D
Explanation:
B aligns model runs with data availability. C ensures Bl dashboards reflect the latest data. A is cost optimization, not a core freshness requirement. D snapshots are more about change tracking than regular updates.
NEW QUESTION # 269
dbt Model Snippet:SQL
Answer: B
Explanation:
Focus on the calculation itself, as the rest of the structure seems expected. The source reference and grouping appear standard.
NEW QUESTION # 270
You need to gather detailed metrics about how your dbt models are used (who runs them, frequency, common failure points, etc.). What kind of tooling would help with this?
Answer: C,D
Explanation:
B is relevant if you use dbt Cloud. C is suitable, as many observability platforms have features for tracking operational data, including dbt job metrics. A and D, while containing information, are likely too granular or require a lot of manual work.
NEW QUESTION # 271
Which two dbt commands work with dbt retry?
Choose 2 options.
Answer: D,E
Explanation:
The correct answers are A: run-operation and E: snapshot.
According to dbt's retry documentation, the dbt retry command works by examining the artifacts of a previous invocation (manifest, run results) and then re-running failed nodes for commands that generate node executions. Only commands that write run results and maintain execution state can be retried. These include dbt run, dbt test, dbt seed, dbt snapshot, and dbt run-operation when the operation executes nodes or macros that generate runtime artifacts.
Option A is correct because run-operation supports retry when the macro being run triggers execution tracked in run results. Option E is correct because snapshots execute SQL against the warehouse and record stateful results, meaning dbt can retry failed snapshots using the retry mechanism.
Options B (parse), C (debug), and D (deps) do not create runnable nodes or execution results; they simply validate or install project resources. These commands do not produce retryable artifacts, so dbt retry cannot operate on them.
Thus, the only options that work with dbt retry from the list provided are run-operation and snapshot.
NEW QUESTION # 272
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