Describe Data Pipeline Best Practices DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   Why is alerting important in a data pipeline?

   • AIt automatically retries failed tasks
   • BIt reduces the need for monitoring
   • CIt prevents all pipeline failures
   • DIt notifies the team when a pipeline fails or performs poorly
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2. 27foundation · easy

   How can parallel processing improve a data pipeline?

   • ABy processing multiple chunks of data simultaneously
   • BBy eliminating the need for data validation
   • CBy reducing the amount of data to process
   • DBy simplifying the pipeline code
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4. 28application · medium

   A data engineering team maintains a Python pipeline that ingests CSV files, cleans them, and loads them into a warehouse. Recently, a new analyst requested a variation that filters out a specific category before cleaning. The team wants to reuse the existing cleaning logic without duplicating code. Which approach best aligns with pipeline best practices?

   • AWrite a new pipeline from scratch that includes both filtering and cleaning, and deprecate the old one.
   • BRefactor the cleaning logic into a shared module and call it from both pipelines, applying the filter as a separate step.
   • CModify the original pipeline to include the filter, and have the analyst use the same script.
   • DCopy the cleaning function into the new script and add the filter at the top.
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5. 29foundation · easy

   What is a key advantage of writing reusable functions for common pipeline operations?

   • AIt makes the pipeline run faster
   • BIt reduces code duplication and promotes consistency
   • CIt eliminates the need for documentation
   • DIt guarantees the pipeline is bug-free
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6. 30application · medium

   A pipeline runs a series of transformations. When a transformation fails, the team wants to know exactly which step failed and why, without having to sift through the entire log. What should they implement?

   • AUse structured logging with a consistent format that includes the step name, timestamp, and error message.
   • BWrite logs to a file without any timestamps.
   • CLog only the final success or failure of the entire pipeline.
   • DPrint the entire stack trace for every step, even on success.
   Reveal Solution Discussion Select an answer first