最新有效的dbt-Analytics-Engineering認證考試培訓材料 -免费的dbt-Analytics-Engineering部分試題下載

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dbt Labs dbt-Analytics-Engineering Exam Syllabus Topics:

Section	Weight	Objectives
dbt Fundamentals	15%	- dbt Core vs dbt Cloud - dbt workflow and best practices - dbt project structure
Deployment and Orchestration	15%	- Environments (dev, staging, prod) - Git version control integration - CI/CD with dbt Cloud - Jobs and scheduling in dbt Cloud
Data Transformation Techniques	25%	- Macros and packages - Common table expressions and subqueries - Jinja templating - Refactoring and incremental models
Testing and Documentation	20%	- Custom data tests - dbt docs and DAG visualization - Schema tests (unique, not_null, accepted_values, relationships) - Documentation generation
Models	25%	- Snapshots - Materializations (table, view, ephemeral, incremental) - Sources and references - Seeds - Writing and managing SQL models

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最新的 Analytics Engineers dbt-Analytics-Engineering 免費考試真題 (Q327-Q332):

問題 #327
You're implementing singular tests (accepted values). After providing a list of valid values, you notice some strange behavior: the test is passing even with records that contain unexpected dat a. What's the most likely issue?

A. The model's data type isn't compatible with the accepted_values test.
B. The accepted_values test configuration has the wrong column specified.
C. A downstream process is overwriting the results after the model is built.
D. Your test case is accidentally permitting null values, which bypass the check.

答案：A,B,D

解題說明：
Each of these directly impacts the accepted_values test's effectiveness, while D is unlikely for a singular test.

問題 #328
Your team establishes a policy for handling different levels of test failures (e.g., warnings vs. critical errors). How could you implement this policy effectively within your dbt workflows?

A. Use custom severity levels in your test definitions and a post-hook to manage actions accordingly.
B. Rely on your data observability platform to differentiate and alert based on dbt test results.
C. Run dbt test multiple times with varying filters based on desired severity levels.
D. Use dbt tags for classifying tests, then selectively execute them.

答案：A,B,D

解題說明：
Each provides a mechanism to act upon varying severity levelsA leverages dbt's built-in features- C integrates with external monitoring tools. D offers flexible execution control.

問題 #329
(Multiple Select)

A. Nesting multiple correlated subqueries within a single query_
B. Using SELECT * to include all columns dynamically
C. Leveraging window functions to calculate values across rows or groups.
D. Writing custom macros to encapsulate frequently used SQL logic.

答案：A,B

解題說明：
SELECT * can lead to performance issues and unexpected results. Correlated subqueries often have better-performing joins equivalents. Macros and window functions are good practices.

問題 #330
You establish a process using dbt tests and snapshots to track data quality changes over time. To maximize the utility of this historical record for troubleshooting, what additional step would be beneficial?

A. Automatically purge snapshots older than a certain date to save storage resources.
B. Schedule regular alerts whenever a test result deviates significantly from previous runs.
C. Integrate metadata about dbt model versions and schema changes alongside the snapshots.
D. Convert the dbt test results and snapshots into time-series tables for easier trend analysis.

答案：B,C,D

解題說明：
These enhance the historical context and make the data actionable for resolving issues- A might discard valuable information prematurely

問題 #331
Which of the following scenarios would typically warrant the use of production data in a development environment?

A. Training new members of your team on writing SQL and basic dbt models.
B. Testing changes to a critical business reporting model for accuracy before deployment.
C. Debugging a logic error that only manifests itself under the exact conditions of real-world production data.
D. Development environments should generally avoid using real production data to minimize risks.

答案：D

解題說明：
While A and C might occasionally have valid use cases for production data in development, it usually should be avoided. Consider these for special circumstances:A Ideally, realistic test data or anonymized production samples should be used. C: Production data snapshots or masked/anonymized versions might be necessary in rare situations.

問題 #332
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