AI0-001 AI Security Practice Question
A company is developing an AI-powered recruitment tool. To prevent bias and ensure fairness, they want to audit the model's training data and outputs. Which TWO practices should they implement as part of secure AI development?
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
✓
Threat modeling using STRIDE for AI-specific threats
Threat modeling for AI systems helps identify bias-related threats, and access controls on training data prevent unauthorized modifications that could introduce bias. Both are part of secure AI development practices.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enabling model parallelism
Why it's wrong here
Model parallelism is a performance optimization, not a security or fairness control.
- ✓
Threat modeling using STRIDE for AI-specific threats
Why this is correct
STRIDE can be applied to identify threats like tampering with training data leading to bias.
- ✗
Increasing the model's learning rate
Why it's wrong here
Learning rate affects convergence but does not address bias or security.
- ✓
Implementing access controls on the training dataset
Why this is correct
Access controls ensure only authorized personnel can alter training data, reducing bias injection.
- ✗
Using a larger batch size
Why it's wrong here
Batch size is a training hyperparameter, not a security practice.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.