- A
Model bias leading to unfair outcomes
Why wrong: Bias is important but explainability is often a regulatory requirement for automated decisions.
- B
Data privacy during model training
Why wrong: Data privacy is important but explainability is more directly tied to credit decision regulations.
- C
Adversarial attacks on the model
Why wrong: Adversarial attacks are a security risk but not the most critical for regulatory compliance.
- D
Lack of model explainability for automated decisions
Many regulations require explanations for automated credit decisions.
Question 140 of 1,000
CRISC Information Technology and Security Practice Question
This CRISC practice question tests your understanding of information technology and security. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
An organization is adopting machine learning for credit scoring decisions. Which of the following risks is MOST critical from a regulatory compliance perspective?
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
✓
Lack of model explainability for automated decisions
Regulatory frameworks like GDPR and the EU AI Act emphasize the right to explanation for automated decisions. Without model explainability, the organization cannot demonstrate compliance with transparency requirements, making it the most critical risk from a regulatory compliance perspective.
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.
- ✗
Model bias leading to unfair outcomes
Why it's wrong here
Bias is important but explainability is often a regulatory requirement for automated decisions.
- ✗
Data privacy during model training
Why it's wrong here
Data privacy is important but explainability is more directly tied to credit decision regulations.
- ✗
Adversarial attacks on the model
Why it's wrong here
Adversarial attacks are a security risk but not the most critical for regulatory compliance.
- ✓
Lack of model explainability for automated decisions
Why this is correct
Many regulations require explanations for automated credit decisions.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between operational risks (like bias or adversarial attacks) and regulatory compliance risks, where explainability is the primary legal requirement for automated decision systems.
Detailed technical explanation
How to think about this question
Explainability in machine learning often relies on techniques like SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to provide feature importance scores for individual predictions. Under the GDPR's Article 22, individuals have the right not to be subject to a decision based solely on automated processing, and if such processing occurs, the controller must provide meaningful information about the logic involved. In credit scoring, a lack of explainability can lead to regulatory fines and legal challenges, as seen in cases where black-box models were found to violate fair lending laws.
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
A practitioner preparing for the CRISC exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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 CRISC question test?
Information Technology and Security — This question tests Information Technology and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Lack of model explainability for automated decisions — Regulatory frameworks like GDPR and the EU AI Act emphasize the right to explanation for automated decisions. Without model explainability, the organization cannot demonstrate compliance with transparency requirements, making it the most critical risk from a regulatory compliance perspective.
What should I do if I get this CRISC 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This CRISC practice question is part of Courseiva's free ISACA 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 CRISC exam.
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