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CompTIA DataX DY0-001 (V1) Practice Question

You are preparing a large e-commerce transactions table for a sales-forecasting model. A validation query reveals that every record whose client_app_version equals "3.2.1-legacy" shows order_amount values about 100 × larger than comparable orders (for example, a typical $75 purchase is stored as 7 500).

Mobile-engineering confirms this specific app version sent monetary values in cents instead of dollars; no other rows are affected.

To correct the data while preserving information and maintaining data-lineage metadata, which data-wrangling action should you take?

  • Replace the affected order_amount values with NULL and later impute them with the overall median order value.

  • Drop all rows generated by client_app_version = '3.2.1-legacy' to remove the corrupted records completely.

  • Treat the inflated values as idiosyncratic errors and winsorize order_amount at the 99th percentile across the entire dataset to cap extreme values.

  • Identify the issue as a scale-factor systematic error and divide order_amount by 100 only for rows where client_app_version = '3.2.1-legacy', recording the transformation in the pipeline metadata.

CompTIA DataX DY0-001 (V1)
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