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ISACA AAIA Exam Syllabus Topics:

TopicDetails
Topic 1
  • AI GOVERNANCE AND RISK: It encompasses understanding different AI models and their life cycles, guiding AI strategy, defining roles and policies, managing AI-related risks, overseeing data privacy and governance, and ensuring adherence to ethical practices, standards, and regulations.
Topic 2
  • AI Operations: It covers managing AI-specific data needs—including collection, quality, security, and classification—applying development lifecycle methodologies with privacy and security by design, change and incident management, testing AI solutions, identifying AI-related threats and vulnerabilities, and supervising AI deployments.
Topic 3
  • Auditing Tools and Techniques: This section of the exam measures the skills of AI auditors and centers on auditing AI systems using appropriate tools and methods. It includes audit planning and design, sampling methodologies specific to AI, collecting audit evidence, using data analytics for quality assurance, and producing AI audit outputs and reports, including follow-up and quality control measures.

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ISACA Advanced in AI Audit Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which of the following components would MOST effectively address cumulative benefits as part of reinforcement learning (RL)?

Answer: C

Explanation:
In Reinforcement Learning, the goal is to maximize long-term rewards. While the "Reward Function" provides immediate feedback for an action, the "Value Function" estimates the
"cumulative benefit" or the total expected reward an agent can gain from a particular state moving forward. For an auditor, understanding the value function is crucial because it governs the agent's long-term strategy and decision-making logic. If the value function is poorly defined, the AI might take actions that yield immediate points but lead to long-term failure or unethical shortcuts.


NEW QUESTION # 69
Which of the following is the PRIMARY objective of AI governance?

Answer: A

Explanation:
The AAIATM Study Guide defines the primary objective of AI governance as establishing structure and accountability for AI initiatives. This includes clearly assigning responsibilities across development, deployment, risk management, and auditing roles to ensure that AI is used responsibly and transparently.
"AI governance establishes the policies, roles, and oversight structures that guide the ethical and secure deployment of AI. Clear accountability helps prevent unauthorized use and ensures strategic alignment."


NEW QUESTION # 70
Which of the following is the BEST reason that recurrent neural networks enable language translation of documents?

Answer: A

Explanation:
Recurrent neural networks (RNNs) and their variants (such as LSTMs and GRUs) are designed to handle sequential data, capturing dependencies across time or position in a sequence. In language translation, words and phrases must be interpreted in context, where the meaning of a word depends on preceding (and, in advanced architectures, following) tokens. RNNs maintain internal state across steps, allowing the model to encode information from earlier parts of the sentence when predicting later outputs.


NEW QUESTION # 71
During a walk-through, an IS auditor observes an AI engineer entering a prompt that manipulates the AI model's behavior. Which of the following is the BEST control to prevent this?

Answer: A

Explanation:
The most direct and effective control for preventing prompt-based manipulation is toenforce a structured input/output template(option A). AAIA highlights prompt management as a key emerging control area because unstructured prompts can lead to:
* Prompt injection
* Model manipulation
* Circumvention of rules
* Unauthorized access to sensitive outputs
* Safety violations
Templates constrain user input to predefined formats, reducing opportunities to embed hidden instructions or modify model behavior.
Adversarial training (B) strengthens robustness but doesnotprevent users from inserting manipulative prompts.
Encryption (C) protects data, not prompt integrity.
Immediate retraining (D) addresses performance, not misuse.
Templates ensure consistent, secure, and auditable prompt structure.
References:
AAIA Domain 1: AI Governance, Prompt Controls
AAIA Domain 2: Security Controls for Generative AI


NEW QUESTION # 72
Which of the following BEST supports reproducibility of AI model results during an audit?

Answer: A

Explanation:
Reproducibility requires that the exact code, data snapshot, hyperparameters, and environment used to produce a result can be retrieved and rerun -- this is achieved through rigorous versioning practices (MLOps).


NEW QUESTION # 73
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