AAISM Practice Questions

A.  

Robustness

B.  

Accuracy

C.  

Explainability

D.  

Adaptability

A.  

Penetration testing

B.  

Human-in-the-loop

C.  

AI impact analysis

D.  

Data asset validation

A.  

Attacking the underlying hardware of the AI system

B.  

Providing inputs that mislead the AI model into incorrect predictions

C.  

Reverse engineering the AI model using social engineering techniques

D.  

Conducting denial-of-service (DoS) attacks against AI APIs

A.  

Leveraging selected open-source models

B.  

Conducting periodic data reviews

C.  

Restricting use of data in third-party models

D.  

Maintaining a complete inventory with data lineage records

A.  

Erasing personal data after training

B.  

Ensuring the quality of personal data

C.  

Anonymizing personal data

D.  

Hashing personal data

A.  

Attacking underlying hardware

B.  

Providing inputs that mislead the model into incorrect predictions

C.  

Reverse-engineering the model using social engineering

D.  

Conducting denial-of-service attacks on AI APIs

A.  

Being transparent with customers about the data sources

B.  

Implementing an updated and tested break-glass policy

C.  

Delivering AI-specific security awareness training

D.  

Using training data from multiple sources

A.  

Model hallucinations

B.  

Data leakage

C.  

Lack of monitoring

D.  

Policy violations

A.  

Master services agreement

B.  

Change management

C.  

Shared responsibility model

D.  

Data minimization

A.  

Rely on the AI provider’s independent third-party audit reports for assurance

B.  

Establish policies and awareness training for acceptable use of AI

C.  

Require opt-out provisions for data usage in service agreements

D.  

Establish guidelines and best practices with third parties for intellectual property ownership