AIF-C01 Guidelines for Responsible AI Practice Question
Exhibit
{
"report_version": "1.0",
"pre_training_bias_metrics": [
{
"name": "ClassImbalance",
"value": 0.8,
"threshold": 0.1,
"status": "violated"
},
{
"name": "DemographicParity",
"value": 0.9,
"threshold": 0.1,
"status": "violated"
}
]
}Refer to the exhibit. An AWS customer runs SageMaker Clarify to evaluate bias in their training data. The report shows multiple metrics with status 'violated'. What should the customer do next?
⚠ Common exam trap
A common misconception is that adding more data will automatically reduce bias. Without addressing the specific imbalance or bias source, adding data can amplify existing disparities. The correct approach is to use Clarify's metrics to guide targeted mitigation, such as balancing the dataset.
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
✓
Use data augmentation to balance the dataset
SageMaker Clarify bias metrics such as Class Imbalance (CI) or Difference in Positive Proportions in Labels (DPPL) flag potential bias in the training data or model predictions. When a report shows violations, the next step is to review the findings and apply a targeted mitigation. Among the options, balancing the dataset through data augmentation directly addresses the representative imbalance; reducing features or simply adding more data does not target the demographic imbalance, and ignoring the metrics is not appropriate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use data augmentation to balance the dataset
Why this is correct
Data augmentation can balance representation.
- ✗
Reduce the number of features
Why it's wrong here
Feature reduction does not fix imbalance.
- ✗
Retrain the model with more data
Why it's wrong here
More data does not guarantee balance.
- ✗
Ignore the metrics because thresholds are too strict
Why it's wrong here
Violations should be addressed.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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