Available Number of Questions: Maximum of
90 Questions
Exam Name: ISACA Advanced in AI Risk
Related Certification(s):
Isaca AAIR Certification
Isaca AAIR Exam Topics - You’ll Be Tested in Actual Exam
What surprises most candidates is how often the exam expects you to think in control terms rather than model building terms. You are judged on whether you can spot where risk actually enters the AI life cycle and what evidence would show it is being handled, from data sourcing and labeling through training, validation, deployment, change control, and monitoring. It is less about naming every possible risk and more about tracing cause and effect, like how a weak data intake gate becomes a downstream testing gap and then turns into a production incident that no one can explain. The risk program management angle matters because the exam keeps asking who owns what, how issues are tracked, how exceptions are approved, and how reporting flows so decisions are made before problems harden into accepted practice. Candidates often miss that governance and framework integration is not abstract policy talk. It is about fitting AI risk work into existing enterprise risk routines so life cycle controls, metrics, and escalation paths are consistent with how the organization already runs risk. If you can connect a life cycle control to a program artifact and then to a governance decision point, you are in the right mindset. When you study, practice writing the story of an AI system from intake to monitoring and keep asking what proof you would expect to see at each handoff.
Isaca AAIR Exam Short Quiz
Attempt this Isaca AAIR exam quiz to self-assess your preparation for the actual Isaca ISACA Advanced in AI Risk exam. CertBoosters also provides premium Isaca AAIR exam questions to pass the Isaca ISACA Advanced in AI Risk exam in the shortest possible time. Be sure to try our free practice exam software for the Isaca AAIR exam.
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Isaca AAIR Exam Quiz
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IsacaAAIR
Q1:
A risk practitioner learns that an AI system used by a manufacturer for quality control (QC) has produced inaccurate responses that could potentially impact user safety. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
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AImplement human-in-the-loop reviews.
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BConduct bias and fairness testing.
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CAugment the model with synthetic data.
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DProvide AI prompt engineering training.
IsacaAAIR
Q2:
Which of the following is the GREATEST benefit of incorporating AI technology for data asset management?
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AJustifying the use of synthetic data to augment smaller datasets
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BReducing the initial impacts of data poisoning and exfiltration attacks
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CProviding more rapid identification of overfitting during model training
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DAutomating data cleaning and metadata tagging for large datasets
IsacaAAIR
Q3:
Which of the following is a risk practitioner's BEST justification for embedding AI risk considerations into acceptable use policies?
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AAddressing the potential for shadow AI by defining an allow list for AI tools
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BApplying uniform risk controls across diverse business functions
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CMaintaining alignment of enterprise tolerance across decision-making systems
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DAssigning AI risk accountability to business unit leadership
IsacaAAIR
Q4:
A credit-scoring AI solution exhibits steadily declining accuracy despite unchanged input distributions. Which of the following should a risk practitioner consider to be the GREATEST risk?
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ATechnical delays affecting credit score updates
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BUnderfitting resulting from shortened training cycles
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CConcept drift leading to faulty decisions
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DIncreased model retraining costs
IsacaAAIR
Q5:
An organization has deployed an AI system to automate critical data analysis functions. Which of the following is the MOST appropriate way for the risk practitioner to assess the multiple sources of risk associated with this situation?
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APrioritize the risk factors most likely to generate substantial harm.
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BQuantify the financial impact of competitors' realized risk events on AI initiatives.
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CRate each risk factor independently as a basis for ordering mitigation actions.
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DDocument the exploitable technical limitations of all AI system components.
