A social media company is using a deep learning model to automatically moderate content. The model sometimes flags benign posts as harmful, leading to user complaints. What is the primary risk this model is exhibiting?

Bias risk because the model is unfairly targeting certain types of content

Explainability risk because users cannot understand why posts are flagged

Robustness risk because the model is not resilient to adversarial inputs

Privacy risk due to processing user data

A retail company wants to implement an AI chatbot for customer support. They have a small data science team but no experience with large language models. Their risk appetite is low because incorrect responses could harm customer trust. Which strategy best aligns with their business goals and risk posture?

Adopt an open-source chatbot framework and deploy it without extensive testing to accelerate time-to-market

Purchase a commercial chatbot solution with a restricted response scope and human escalation for uncertain cases

Use a general-purpose public chatbot and provide it with the company's customer data for personalized responses

Build a custom chatbot from scratch using an open-source language model and fine-tune it on their support data

In a healthcare AI use case that predicts patient readmission risk, which governance implication is most critical?

Maximizing the model's training speed

Ensuring the model is explainable to clinicians and regulators

Using the most complex model available to improve accuracy

Reducing the number of features to simplify the model

A data science team is developing a computer vision model for quality inspection. They need to collaborate on the model architecture, track experiments, and ensure reproducibility. Which framework or tool combination would best support these needs?

Use a Jupyter notebook for development and save model files to a shared drive

Use TensorFlow exclusively for both development and experiment tracking

Use ONNX for model development and a shared spreadsheet for tracking experiments

Use PyTorch for model development and MLflow for experiment tracking and model versioning

Which approach best ensures that an organization's AI initiatives are aligned with its business goals?

Focusing only on AI projects that are easy to implement

Prioritizing AI projects that directly support strategic objectives and have clear success metrics

Selecting AI projects based on the latest technology trends

Deploying AI in every department to maximize innovation

ISACAAdvanced in AI Risk

Domain 1Objective 1

AI Models, Frameworks, Strategies, and Use Cases AAIR Practice Questions (Page 1)

Part of the AI Risk Governance and Framework Integration domain, which accounts for 37% of the AAIR exam.

32questions here

7free pages

7concepts

37%of the exam

Questions 1–5

1application · medium
A social media company is using a deep learning model to automatically moderate content. The model sometimes flags benign posts as harmful, leading to user complaints. What is the primary risk this model is exhibiting?
Select an answer first
2application · medium
A retail company wants to implement an AI chatbot for customer support. They have a small data science team but no experience with large language models. Their risk appetite is low because incorrect responses could harm customer trust. Which strategy best aligns with their business goals and risk posture?
Select an answer first
3foundation · easy
In a healthcare AI use case that predicts patient readmission risk, which governance implication is most critical?
Select an answer first
4application · medium
A data science team is developing a computer vision model for quality inspection. They need to collaborate on the model architecture, track experiments, and ensure reproducibility. Which framework or tool combination would best support these needs?
Select an answer first
5foundation · easy
Which approach best ensures that an organization's AI initiatives are aligned with its business goals?
Select an answer first

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