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AIF-C01 practice question: Use cases and ML techniques (2 of 3)

Fundamentals of AI and ML A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A payroll team must calculate employee tax withholding each pay run using rates that tax law fixes and publishes every year.

Auditors need each result to be exactly reproducible from the same inputs. A manager suggests training an ML model on last year's payslips instead of writing the calculation.

What is the BEST advice?

Choose one answer.

  1. A. Train a deep learning model, because it will learn the tax rules more precisely than code.
  2. B. Use a foundation model, because it can read the published tax tables and apply them directly to each payslip.
  3. C. Use rule-based code that applies the published rates, since the answer is deterministic and the rules are known.
  4. D. Train a clustering model that groups employees into tax bands, then apply the band rates.
Show the answer and explanation

Answer: C

  • A. Wrong: Deep learning learns approximate patterns from data. It cannot guarantee the exact, auditable result that fixed tax law requires.
  • B. Wrong: A foundation model generates text from learned patterns and can make arithmetic or reading errors. It is not a reproducible calculation engine for legal tax figures.
  • C. Right: Correct. Fixed rules in code give the same answer every time, and auditors can read the logic. An ML model adds nothing when the rules are already known and published.
  • D. Wrong: Clustering finds groups in data without labels and does not calculate tax. Grouping employees does not give the exact withholding amount that payroll must pay.

Explanation

When the rules are fixed, known and must be reproducible, ordinary code is the right tool. Machine learning is useful when the rules are unknown or too complex to write down, not when they are published. This question tests recognizing when ML is the wrong choice.

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