- A
SHAP (SHapley Additive exPlanations) values from the test predictions.
Why wrong: SHAP values provide local explanations, not global bias metrics.
- B
A re-run of the training job with a fairness constraint.
Why wrong: Training with fairness constraints addresses bias but does not detect existing bias.
- C
Pre-training bias metrics like Class Imbalance (CI) and Difference in Positive Proportions in Labels (DPPL).
Pre-training metrics identify bias in the training data that could lead to unfair models.
- D
Feature importance values after training.
Why wrong: Feature importance shows which features influence predictions but does not directly measure bias.
Question 925 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A machine learning team wants to detect bias in a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
A machine learning team wants to detect bias in a binary classification model before deployment. They use SageMaker Clarify. Which type of bias metric should they compute to understand whether the model treats different demographic groups unfairly in predictions?
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
✓
Pre-training bias metrics like Class Imbalance (CI) and Difference in Positive Proportions in Labels (DPPL).
SageMaker Clarify can compute both pre-training and post-training bias metrics. Pre-training bias metrics like Class Imbalance (CI) and Difference in Positive Proportions in Labels (DPPL) measure bias in the dataset before the model is trained, which is essential for understanding whether the model will treat different demographic groups unfairly based on inherent label imbalances. These metrics directly assess whether the training data itself contains systematic disparities that could lead to unfair predictions.
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.
- ✗
SHAP (SHapley Additive exPlanations) values from the test predictions.
Why it's wrong here
SHAP values provide local explanations, not global bias metrics.
- ✗
A re-run of the training job with a fairness constraint.
Why it's wrong here
Training with fairness constraints addresses bias but does not detect existing bias.
- ✓
Pre-training bias metrics like Class Imbalance (CI) and Difference in Positive Proportions in Labels (DPPL).
Why this is correct
Pre-training metrics identify bias in the training data that could lead to unfair models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Feature importance values after training.
Why it's wrong here
Feature importance shows which features influence predictions but does not directly measure bias.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse post-hoc explainability methods (like SHAP) with bias detection metrics, or they assume that bias can only be measured after training, when in fact pre-training metrics like CI and DPPL are specifically designed to catch bias in the data before model training begins.
Trap categories for this question
Command / output trap
Feature importance shows which features influence predictions but does not directly measure bias.
Detailed technical explanation
How to think about this question
Pre-training bias metrics such as CI and DPPL are computed directly from the dataset's labels and sensitive attributes (e.g., race, gender) without involving the model. CI measures the ratio of the most frequent class label to the least frequent class label, while DPPL measures the difference in the proportion of positive labels between the favored and unfavored demographic groups. These metrics are critical because if the training data is biased, even a perfectly trained model will propagate that bias, making pre-training detection a first line of defense in the SageMaker Clarify workflow.
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.
TExam Day Tips
- 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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Pre-training bias metrics like Class Imbalance (CI) and Difference in Positive Proportions in Labels (DPPL). — SageMaker Clarify can compute both pre-training and post-training bias metrics. Pre-training bias metrics like Class Imbalance (CI) and Difference in Positive Proportions in Labels (DPPL) measure bias in the dataset before the model is trained, which is essential for understanding whether the model will treat different demographic groups unfairly based on inherent label imbalances. These metrics directly assess whether the training data itself contains systematic disparities that could lead to unfair predictions.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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