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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: dbt Models Governance | 15% | - Naming conventions and standards - Project organization and structure - Version control integration |
| Topic 2: Developing dbt Models | 20% | - Model design and structure
|
| Topic 3: Leveraging dbt State | 5% | - State-aware operations - State management |
| Topic 4: External Dependencies | 10% | - Managing snapshots - Using packages - External sources integration |
| Topic 5: Creating and Maintaining Documentation | 10% | - Descriptions and metadata - Generating documentation - Documentation standards |
| Topic 6: Debugging and Error Resolution | 15% | - Debugging techniques - Identifying modeling errors - Resolving data quality issues |
| Topic 7: Implementing dbt Tests | 10% | - Test configuration and execution - Custom tests - Built-in tests |
| Topic 8: Managing Data Pipelines | 15% | - Pipeline orchestration - Deployment strategies - CI/CD integration |
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NEW QUESTION # 201
(Multiple Select)
Answer: A,C,D
Explanation:
A reflects the increased security needs of production environments. B allows scaling or optimization based on workload. C prevents accidental changes in production. D is a strategy, but not a fundamental difference.
NEW QUESTION # 202
Which two are true for a dbt retry command?
Choose 2 options.
Answer: D,E
Explanation:
The correct answers are B and E.
The dbt retry command was introduced to help developers quickly re-run only what failed in the prior dbt invocation. According to dbt documentation, dbt retry examines metadata from the previous execution, including the run results and the manifest.json, to determine which nodes failed. This aligns with Option E, as dbt uses the manifest to identify the models, tests, and other nodes that errored or were not executed successfully.
Option B is also correct because dbt retry works only when the previous run failed due to execution errors (e.
g., a warehouse failure or a logic error in SQL). If the failure was due to a syntax error that prevented dbt from compiling, there will be no valid manifest produced, so retry cannot execute. Thus, dbt retry can only re- run models when the previous failure occurred after successful compilation.
Options A and C are incorrect because retry does not run all nodes nor does it automatically pick up exactly where an execution stopped - instead, it runs failed and downstream unbuilt nodes as determined by the manifest. Option D is also incorrect; retry uses the execution metadata, not selectors, to determine what should run.
NEW QUESTION # 203
Is this materialization supported by Python models in dbt?
Ephemeral
Answer: A
Explanation:
dbt Python models support a limited set of materializations because they rely on execution within the data platform's Python compute engine (such as Snowpark for Snowflake, Dataproc for BigQuery, or Spark).
These engines require models to materialize into actual relations-tables or views-in order to persist the results of Python-based transformations.
The ephemeral materialization, however, is fundamentally incompatible with this behavior. Ephemeral models do not create relations in the warehouse; instead, dbt inlines their SQL logic directly into downstream models. Since Python models cannot be inlined (they execute Python code, not SQL), dbt does not allow ephemeral Python models. dbt requires Python model outputs to be materialized as either:
* table
* view
* incremental
Therefore, ephemeral is not supported for Python models, and attempting to configure a Python model as ephemeral will result in a compilation error.
The reason is straightforward: ephemeral logic depends on SQL compilation, while Python models depend on executing Python code in the data platform. Because these mechanisms are incompatible, dbt restricts Python models to relational materializations only.
Thus, the correct answer is No - ephemeral is not supported for Python models.
NEW QUESTION # 204
"Sometimes columns that should have data are showing null values!" exclaims a colleague. What initial debugging steps would you suggest beyond examining the model logic itself?
Answer: D
Explanation:
Don't limit yourself to the single model in question! Data flows throughout the project, and changes elsewhere might have cascading effects.
NEW QUESTION # 205
You've identified the same complex date calculations repeated across multiple models. Which approach aligns with modularity and DRY (Don't Repeat Yourself) principles?
Answer: A
Explanation:
Macros excel in encapsulating reusable logic, promoting modularity and avoiding code repetition. While option C could work, it introduces unnecessary materialization.
NEW QUESTION # 206
......
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