dbt-Analytics-Engineering Practice Guide & dbt-Analytics-Engineering Valid Test Prep
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dbt Labs dbt Analytics Engineering Certification Exam Sample Questions (Q271-Q276):
NEW QUESTION # 271
Your team sometimes defines raw data sources directly in dbt models using .yml files within the models directory. When might this approach be problematic?
- A. If the raw dataset requires complex transformations.
- B. If multiple models utilize the same raw dataset.
- C. If the raw dataset is accessed via a non-standard data warehouse connector.
- D. If the raw dataset's schema is subject to change.
Answer: B
Explanation:
Defining raw sources within models leads to duplication and potential maintenance issues when multiple models share that dependency. A separate sources.yml file is generally preferred for shared datasets.
NEW QUESTION # 272
You suspect discrepancies due to potential changes in upstream source tables. What's a practical first step to investigate?
- A. Compare the current state of the source tables against their historical snapshots.
- B. Contact the team responsible for managing the sources to inquire about recent updates.
- C. Review the dbt project's manifest.json for related changes.
- D. Run all dbt models and examine the logs for unusual entries.
Answer: B
Explanation:
Understanding if there were any intentional data source changes is crucial before diving into technical troubleshooting. Options A, B, and D might be necessary subsequent steps.
NEW QUESTION # 273
You have strict data retention policies. Which factors might influence how data is managed differently across your production, development, and raw data environments?
- A. Raw data might be archived or deleted following a shorter retention period than production data
- B. Regulatory requirements could mandate that production data be retained for longer periods.
- C. Development data might have less stringent retention requirements, allowing for more frequent deletion-
- D. All of the above.
Answer: D
Explanation:
A: Raw data can often be discarded post-processing, reducing storage costs. B: Regulations often dictate long-term retention for production data. C: Development data is often transient and subject to less strict retention.
NEW QUESTION # 274
Which two are true for a dbt retry command?
Choose 2 options.
- A. It reruns all nodes in your previous invocation statement.
- B. It reads a manifest.json file to identify the models and tests that failed in the last run.
- C. It reuses selectors from the previous command.
- D. It picks up from the error without running all of the upstream dependencies.
- E. It retries the previous command if it is not a syntax error in a model.
Answer: B,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 # 275
You're considering adding workflow orchestration to manage dependencies between your dbt project, data loading tasks, and the triggering of downstream Bl updates. Which of the following types of tools would be most suitable for this?
- A. Data quality monitoring tools
- B. Data lineage tools
- C. CI/CD platforms
- D. General-purpose workflow orchestration tools (e.g., Airflow, Prefect)
Answer: D
Explanation:
While options B (CIICD) and C (data quality monitoring) are potentially relevant parts of the ecosystem, general-purpose workflow orchestration tools are directly designed to manage dependencies and task sequences across various technologies.
NEW QUESTION # 276
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