When cleaning data, what role does using clones play in specific use-cases?

Question

Options

• AClones help preserve original data for audit purposes only
• BClones slow down the data cleaning process significantly
• CClones aid in isolating and resolving data anomalies
• DClones are unnecessary for data cleaning tasks

• A
  8% (4)
• B
  2% (1)
• C
  86% (43)
• D
  4% (2)

Explanation

Using clones in data cleaning means creating a copy of the dataset (or a subset) so you can safely experiment with transformations, test anomaly fixes, and validate results without risking the integrity of the source data - making option C correct. If a fix goes wrong, the original remains untouched and you simply discard the clone, which makes isolating and resolving anomalies (outliers, duplicates, corrupt records) both safer and repeatable.

Why the distractors fail:

• A is too narrow - preserving data for audits is a side benefit, not the primary role clones play in active data cleaning workflows.
• B is the opposite of reality; clones enable parallel, non-blocking experimentation that can speed up the resolution process.
• D contradicts best practice - working directly on production or raw data without a clone is a common source of irreversible data loss.

Memory tip: Think of clones as a "sandbox" - just as developers test code in a sandbox before touching production, data engineers clean in a cloned dataset before touching the source. Clone → Clean → Confirm → Commit.

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