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Test the new category and retrain only if defined evidence: Which action is correct?

AIGP Understanding How to Govern AI Development Medium

Retraining should follow validated performance or context evidence, not an automatic calendar or a single reassuring metric.

The question

A retailer’s demand-planning model is monitored monthly. Forecast error has stayed within tolerance, but a new product category creates a sustained input pattern outside tested conditions. The operations team proposes automatic quarterly retraining; the model owner must recommend the control. Which action is correct?

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  1. Retrain quarterly regardless of observed conditions.
    A calendar schedule may waste resources and can introduce untested behavior without showing that retraining addresses the changed conditions.
  2. Keep the model unchanged because forecast error remains within tolerance.
    Acceptable aggregate error does not establish reliability for a new, untested input regime or reveal subgroup and category-specific effects.
  3. Replace the model immediately because any novel input invalidates prior evaluation.
    Novel inputs require reassessment, but they do not automatically prove the model is unusable or require immediate replacement.
  4. Test the new category and retrain only if defined evidence supports intervention. ✓
    This links retraining to contextual validation and explicit thresholds, rather than treating time or a single metric as decisive.
The trap
Look for the option that connects changed conditions to testing and explicit intervention criteria.

How to remember it

Retraining should follow validated performance or context evidence, not an automatic calendar or a single reassuring metric.

How many of these would you get right?

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More Understanding How to Govern AI Development questions

Part of the Certsqill AIGP question bank · Understanding How to Govern AI Development · Every answer, right and wrong, comes with its own explanation.