A classic, more deterministic-leaning model tends: Which consideration about model type is most relevant to
Output consistency depends on how deterministic the model is; generative variability complicates reproducible auditing.
The question
A compliance team needs an AI tool whose outputs are consistent and reproducible so that each decision can be re-examined during audits. Which consideration about model type is most relevant to meeting this requirement?
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- A classic, more deterministic-leaning model tends to produce consistent, reproducible outputs, whereas a generative model's variability complicates auditing a compliance decision. ✓Correct because output consistency is tied to how deterministic the model behaves, which aids reproducible auditing.
- A larger parameter count always guarantees more reliable compliance decisions, so the biggest available model should simply be selected by default for the task.Wrong because size does not guarantee reliability or the reproducibility the audit requirement demands.
- An open-source license alone ensures outputs are auditable, since anyone can inspect the published source code regardless of the model's underlying probabilistic runtime behavior overall.Plausible but wrong because license openness does not make probabilistic outputs consistent or reproducible.
- A multimodal model is preferable because processing more input types inherently makes each individual decision easier to justify to auditors reviewing the system.Plausible but wrong because handling more input types does not improve reproducibility of decisions.
The trap
Assuming a bigger model or an open-source license makes outputs reproducible and auditable. How to remember it
Output consistency depends on how deterministic the model is; generative variability complicates reproducible auditing.
How many of these would you get right?
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Every answer, right and wrong, comes with its own explanation.
