Which SageMaker Clarify Bias Metric Exceeds the Threshold?
Exhibit
{
"pre_training_bias_metrics": {
"dpl": {
"value": 0.15,
"threshold": 0.1
},
"class_imbalance": {
"value": 0.3,
"threshold": 0.5
},
"label_imbalance": {
"value": 0.4,
"threshold": 0.6
}
}
}Refer to the exhibit. A data scientist runs SageMaker Clarify on a training dataset and receives the above JSON output. Which bias metric exceeds its threshold?
Quick Answer
The answer is DPL (Demographic Parity Difference), as its value of 0.15 exceeds the threshold of 0.1. This metric measures the difference in the probability of a positive outcome between advantaged and disadvantaged groups, and a value above the threshold indicates significant bias in the model’s predictions. On the AWS Certified AI Practitioner AIF-C01 exam, you are often given a SageMaker Clarify JSON output and asked to identify which bias metric violates its threshold, testing your ability to compare numeric values against defined limits. A common trap is confusing DPL with class imbalance or label imbalance, but remember that DPL focuses on prediction outcomes, not dataset composition. For a quick memory tip, think “DPL = Difference in Positive Likelihood” and always check if it exceeds 0.1 first.
⚠ Common exam trap
AWS often tests the specific threshold values for each bias metric (e.g., DPL > 0.10, Label Imbalance > 0.20, Class Imbalance > 0.10) to trick candidates into thinking all metrics must be checked equally, when in fact only the metric exceeding its defined threshold is flagged.
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
✓
DPL
The JSON output shows DPL (Demographic Parity Difference) = 0.15, which exceeds the commonly used threshold of 0.10. DPL measures the difference in the probability of a favorable outcome between advantaged and disadvantaged groups; a value above 0.10 indicates significant bias. The other metrics (Label Imbalance = 0.05, Class Imbalance = 0.02) are below their respective thresholds, so only DPL triggers the violation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
DPL
Why this is correct
In SageMaker Clarify bias reports, the Difference in Positive Proportions in Labels (DPL) metric exceeding its configured threshold flags the largest disparity between facets. That breach identifies DPL as the metric failing its acceptable limit.
- ✗
Label Imbalance
Why it's wrong here
Label imbalance value 0.4 is below threshold 0.6.
- ✗
Class Imbalance
Why it's wrong here
Class imbalance value 0.3 is below threshold 0.5.
- ✗
All metrics exceed thresholds
Why it's wrong here
Only DPL exceeds; class and label imbalance are within limits.
Quick reference
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Same concept, more angles
1 more way this is tested on AIF-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Refer to the exhibit. A data scientist used SageMaker Clarify to evaluate bias in a binary classification model predicting loan approval. The exhibit shows bias metrics for the female facet. What does the analysis indicate about the model's impact on the female group?
medium- A.The metrics are within acceptable thresholds, so no action is needed.
- B.The model shows a high positive bias toward the female group.
- ✓ C.The model has a post-training accuracy difference indicating a negative bias against the female group.
- D.The model exhibits a pre-training class imbalance but no post-training bias.
Why C: The exhibit shows a post-training accuracy difference metric (e.g., difference in positive predicted values or accuracy) that is negative for the female facet, indicating the model's predictions are less accurate for the female group compared to the overall or male group. This negative difference signifies a bias against the female group, as the model performs worse for them after training. SageMaker Clarify computes such metrics to detect disparate impact, and a negative value here directly points to adverse treatment.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.