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Data Cleanup · interview question

What is the healthiest performance judgment around normalization pipelines?

Multiple choiceMediumchapter-4-curatedpremiumdata-cleanupnormalization-pipelines
python
def normalize_row(row: dict[str, str]) -> dict[str, object]:
    return {
        "sku": row["sku"].strip(),
        "quantity": int(row["quantity"]),
    }
Choose one answer
Answer and explanation

Correct answer

B. Measure before optimizing, then change the part that actually dominates cost instead of guessing from style alone.

Why

Premature optimization is usually weaker than targeted optimization backed by measurement.

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