AI Lifecycle Risk Management

Which TWO actions should be taken when a model exhibits significant drift?

An AI risk practitioner is designing the human oversight component for a generative AI assistant used by customer service agents. The assistant drafts responses that agents may send after review. Which TWO controls most directly reduce the risk of harmful or non-compliant outputs reaching customers? (Choose two.)

A developer is using 'Shadow Deployments' for a new AI model. What is the main risk being addressed?

Why is 'Explainability' considered a key risk management control in the AI lifecycle?

A practitioner is reviewing the 'Model Card' for an AI system. What is the primary purpose of this artifact in the AI lifecycle risk management process?

Which THREE risks are associated with 'Automated Deployment' of AI models without a human-in-the-loop?

During the AI lifecycle, when should a 'Data Quality' assessment be performed to minimize long-term risk?

Which THREE factors should be monitored to detect 'Model Drift' in the deployment phase?

Which TWO of the following are key components of a robust AI lifecycle risk management program?

During the 'Monitoring' phase, which activity is most critical for identifying model 'feedback loops'?

Which THREE artifacts should be reviewed during a post-incident AI risk analysis?

In the context of the Google Cloud Vertex AI Model Registry, what is the best approach to mitigate the risk associated with a model update that performs poorly on edge cases?

You are managing a model that utilizes 'Online Learning'. What is the most critical risk requiring constant lifecycle vigilance?

A practitioner is setting up a model monitoring service in AWS SageMaker Model Monitor. They observe that the ground truth data is significantly delayed. What action ensures risk identification remains effective?

Which TWO factors are essential when documenting a model for risk management?

A team notices that an AI model's performance decreases when the input data contains abbreviations that were not present in the training set. This is a risk associated with which lifecycle stage?

A company is implementing 'Human-in-the-Loop' (HITL) for its AI decision system. What is the primary risk of relying on HITL?

You are performing a 'Model Drift' analysis. Which metric is most indicative of performance degradation without access to real-time ground truth?

You are auditing a model's lifecycle and discover that the training data distribution changes every time the model retrains. What is the biggest risk here?

You are assessing risk for an AI model that uses 'Transfer Learning'. What is the most critical risk to manage regarding the pre-trained base model?

An insurance firm's fraud-detection model was approved after validation on a held-out test set. Six months later, investigators find the model performs poorly on claims submitted through a newly launched mobile channel. Which lifecycle risk management activity should have identified this gap before it affected production?

Which THREE considerations must be addressed when designing a 'Training' pipeline to minimize data quality risk?

During the 'Training' phase, why is 'Data Splitting' (train/validation/test) crucial for risk management?

A practitioner is managing AI risk in a CI/CD pipeline. What should be the final gate before model promotion to production?

Which TWO of the following techniques help mitigate bias in the AI lifecycle?

Which TWO of the following scenarios represent 'Data Quality Risk'?

When managing AI lifecycle risk for a model in a regulated industry, which artifact serves as the most important audit trail for the model's provenance?

Which TWO metrics are standard in 'Monitoring' for identifying performance-related AI risks?

A team is designing an AI lifecycle monitoring system. Which metric is most effective for detecting 'Out-of-Distribution' (OOD) risks?

Which THREE factors contribute to 'Data Quality Risk' during the training phase?

What is the primary risk of 'Model Complexity' in the context of the AI lifecycle?

Which AI lifecycle stage is most susceptible to the risk of 'data leakage' where training data inadvertently contains information from the future/target?

An insurer deploys a claims-triage model built by an external vendor. The contract grants the insurer use rights but the vendor refuses to disclose training data composition or feature engineering logic, citing trade secrets. Internal audit must document residual risk before the model enters production. Which action best fulfills the AI risk management obligation given these constraints?

What is the primary risk objective of 'Model Versioning' in the AI lifecycle?

A hospital deploys a sepsis-prediction model. The AI risk manager learns that the vendor retrained the model on new patient data but the hospital's integration still calls the same endpoint, and no notification was issued. Which lifecycle risk control failure does this most directly represent?

A government agency uses an AI system to triage benefit applications. The risk register lists 'automation bias' as a concern. Which monitoring metric would most directly evidence whether this risk is materialising in practice?

A retail bank's credit-scoring AI was trained on three years of historical loan data. During an ISACA AAIR-aligned risk review, the risk practitioner discovers that the model's training dataset included the final repayment outcomes of loans that were still open at the time of data extraction. Which risk is most directly introduced by this dataset construction?

During the design phase of a machine learning model, a practitioner identifies that the training dataset has significant underrepresentation of a minority demographic. What is the most effective proactive risk mitigation strategy?

To mitigate 'Training-Serving Skew' in the AI lifecycle, what is the best practice for data processing pipelines?

When a 'Data Drift' alert is triggered in a production environment, what is the first step in the risk management lifecycle?

A financial institution uses an AI model to approve loans. After deployment, they notice the model is rejecting loan applications from a specific region at a rate 30% higher than historical human benchmarks. What is the most appropriate next step in the risk lifecycle?

A retail bank's credit-scoring AI was validated on 2023 applicant data and deployed in early 2024. By Q4 2024, portfolio default rates among approved loans have crept up while the model's input feature distributions remain statistically unchanged. The risk team wants to isolate the most probable lifecycle cause before retraining. Which risk management activity best addresses this situation?

Which of the following is an example of 'Data Quality Risk' in the AI lifecycle?

Which THREE strategies are effective for mitigating 'Adversarial Risk' in an AI lifecycle?

In the context of the 'AI Lifecycle', what is the risk of 'Model Decay'?

A company is integrating an AI model that uses 'Online Inference'. What is the most significant risk regarding model security?

Which of the following is a primary risk during the 'Deployment' stage of the AI lifecycle?