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Exam Certified Data Engineer Professional topic 1 question 212 discussion

Actual exam question from Databricks's Certified Data Engineer Professional
Question #: 212
Topic #: 1
[All Certified Data Engineer Professional Questions]

A team of data engineers are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks. One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.

What approach would allow them to do this?

  • A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
  • B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
  • C. Maintain data quality rules in a separate Databricks notebook that each DLT notebook or file can import as a library.
  • D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
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Suggested Answer: D 🗳️
by benni_ale at Dec. 8, 2024, 6:17 p.m.

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benni_ale
Highly Voted 1 month, 4 weeks ago
Selected Answer: D
https://docs.databricks.com/en/delta-live-tables/expectations.html "You can maintain data quality rules separately from your pipeline implementations. Databricks recommends storing the rules in a Delta table with each rule categorized by a tag."
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arekm
Most Recent 1 month ago
Selected Answer: D
D is what Databricks suggests as of now
upvoted 1 times
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Thameur01
1 month, 4 weeks ago
Selected Answer: C
To reuse repetitive data quality rules across multiple tables in a Delta Live Tables (DLT) pipeline, the most efficient approach is to maintain these rules in a separate notebook or Python module and import them where needed. This promotes code reusability, maintainability, and consistency
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