AIF-C01 Guidelines for Responsible AI Practice Question

Exhibit

Refer to the exhibit.
```
2023-09-15T14:23:10Z Model endpoint my-model received input: {"features": [0.5, 0.8, 0.2]}, prediction: 1, probability: 0.92
2023-09-15T14:23:11Z Model endpoint my-model received input: {"features": [0.5, 0.8, 0.2]}, prediction: 1, probability: 0.93
2023-09-15T14:23:12Z Model endpoint my-model received input: {"features": [0.5, 0.8, 0.2]}, prediction: 1, probability: 0.91
```

Refer to the exhibit. A developer is reviewing CloudWatch Logs for a deployed model and notices the same input appears multiple times with slightly different probabilities. What responsible AI concern does this pattern suggest?

⚠ Common exam trap

The AWS AI Practitioner exam often tests the distinction between robustness (consistency for the same input) and other AI concerns like bias or drift, so the trap here is confusing non-deterministic output with data drift or overfitting.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The model is not robust; it produces inconsistent predictions for the same input.

The pattern of the same input producing slightly different probabilities indicates that the model's predictions are not deterministic for identical inputs. This violates the principle of robustness in responsible AI, which requires that a model should produce consistent outputs for the same input under the same conditions. Inconsistent predictions for identical inputs undermine trust and reliability, making the model non-robust.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
The model is overfitting to the training data.
Why it's wrong here
Non-deterministic outputs for identical inputs indicate inconsistent or non-reproducible inference, not overfitting, which shows as poor generalisation to unseen data. Overfitting is tempting because it also degrades reliability, but the symptom here is run-to-run variance. Reproducibility and output stability are the actual concern.
✓
The model is not robust; it produces inconsistent predictions for the same input.
Why this is correct
Inconsistent probabilities for identical inputs indicate the model lacks robustness, directly matching the stem's repeated-input pattern. Non-determinism at inference, whether from sampling, dropout, or unstable weights, breaches the reliability principle of responsible AI, which expects stable, reproducible outputs for the same input.
✗
The model is exhibiting bias against a demographic group.
Why it's wrong here
Varying probabilities for the same input indicate non-deterministic inference, not demographic bias, which requires comparing outcomes across protected groups. Bias is tempting because responsible AI often concerns fairness, but no group attribute appears in the logs. The concern is reproducibility and consistent model behaviour.
✗
The input data is drifting from the training distribution.
Why it's wrong here
Repeated inputs yielding varying probabilities indicates non-determinism or inconsistent model outputs, not data drift, which concerns changes in input distribution over time. Drift detection is tempting because CloudWatch Logs can surface distribution shifts, but the pattern here is output variance for identical inputs.

Go deeper

AIF-C01 Guidelines for Responsible AI Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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