MLA-C01 ML Model Development Practice Question
A company wants to use SageMaker Clarify to analyze bias in their training data and model predictions. Which TWO types of bias can Clarify detect? (Choose TWO.)
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
SageMaker Clarify organizes its bias metrics into two categories that map directly to the two marked options: pre-training bias and post-training bias. Option B (Pre-training bias) is correct because Clarify analyzes the training dataset itself before model training, computing metrics such as Class Imbalance (CI), Difference in Proportions of Labels (DPL), and Kolmogorov-Smirnov (KS) to detect imbalances or label skew in the input data. Option E (Post-training bias) is correct because Clarify evaluates model predictions after training, using metrics like Disparate Impact (DI), Difference in Conditional Acceptance (DCA), and Accuracy Difference (AD) to detect bias in predicted outcomes across groups. The remaining options are not Clarify bias categories: algorithmic bias (A) is a general concept rather than a Clarify metric group, while inference bias (C) and deployment bias (D) are not terms Clarify uses to classify the bias it detects.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Algorithmic bias
Why it's wrong here
SageMaker Clarify detects bias in data and models, but algorithmic bias is not one of its named bias categories. Clarify measures pre-training bias metrics such as class imbalance and post-training metrics such as disparate impact. Algorithmic bias is tempting as a general term, but Clarify's documented types are facets like label bias and prediction bias.
- ✓
Pre-training bias
Why this is correct
Pre-training bias measures imbalance or skew in the training dataset itself, such as label or feature distribution disparities, before any model is fitted. Clarify reports this alongside post-training bias, satisfying the requirement to analyse bias in training data.
- ✗
Inference bias
Why it's wrong here
Inference bias is not a SageMaker Clarify bias category; Clarify reports pre-training and post-training bias metrics, not a type called inference bias. It detects label bias, class imbalance, and prediction bias. Inference bias is tempting because Clarify analyses model predictions, but that analysis is measured through post-training metrics, not this label.
- ✗
Deployment bias
Why it's wrong here
Deployment bias is not a bias type SageMaker Clarify measures; Clarify detects pre-training bias metrics such as class imbalance and post-training bias metrics such as disparate impact. It is tempting because deployment-stage skew feels related, but Clarify's scope is data and model prediction bias, not production deployment monitoring.
- ✓
Post-training bias
Why this is correct
Post-training bias metrics evaluate model predictions after training, quantifying imbalances in outcomes across facets such as disparate impact. This satisfies the stem's requirement to analyse bias in model predictions, complementing pre-training bias detection on the training data itself.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.