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.
AIF-C01 Guidelines for Responsible AI Practice Question
This AIF-C01 practice question tests your understanding of guidelines for responsible ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
DPL
The DPL value of 0.15 is greater than the threshold of 0.1, indicating significant bias. Class imbalance (0.3) and label imbalance (0.4) are below their respective thresholds.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
DPL (Difference in Positive Proportions in Predicted Labels) exceeds threshold.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Guidelines for Responsible AI — This question tests Guidelines for Responsible AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: DPL — The DPL value of 0.15 is greater than the threshold of 0.1, indicating significant bias. Class imbalance (0.3) and label imbalance (0.4) are below their respective thresholds.
What should I do if I get this AIF-C01 question wrong?
Identify which AIF-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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 post-training Accuracy Difference (AD) of -0.22 indicates that the model's accuracy for the female group is 22% lower than for the male group, representing a negative bias. Pre-training metrics show some imbalance (CI=0.2) and DPL=-0.15 indicating underrepresentation and lower proportion of positive labels, but the post-training metric directly shows performance disparity. Option A is incorrect because AD is present. Option C is incorrect because AD is negative. Option D is incorrect because the threshold typically is 0.1 for bias detection, and -0.22 exceeds it.
Last reviewed: Jun 23, 2026
Question Discussion
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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.
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