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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - AI System Protection
|
| Topic 2: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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NEW QUESTION # 120
Which of the following should an auditor reference when reviewing a company's human resources AI systems for legal non-compliance?
Answer: A
Explanation:
The EU AI Act is legally binding legislation that specifically governs the use of AI systems, including those used in human resources for hiring, promotion, and evaluation. An auditor reviewing AI systems for legal non-compliance must reference this act because it establishes enforceable requirements related to transparency, bias, risk classification, and prohibited practices.
NEW QUESTION # 121
A company wants to reduce IDS false positives. What tuning should occur FIRST?
Answer: B
Explanation:
A behavioral baseline enables effective tuning and alert reduction.
NEW QUESTION # 122
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?
Answer: B
Explanation:
Basic Concept: AI chatbots are designed with safety guidelines and content policies that prevent them from generating harmful, offensive, or inappropriate content. When users find ways to bypass these restrictions through crafted prompts, they have " jailbroken " the model. CompTIA SecAI+ Study Guide covers jailbreaking as a key AI vulnerability category.
Why C is Correct: Jailbreaking is the process of using cleverly crafted prompts to bypass an AI model ' s built- in safety restrictions, content policies, and behavioral guardrails, causing it to produce outputs it was designed to refuse. The scenario describes a chatbot that was designed for automobile configuration assistance but is producing offensive responses following customer prompts, indicating that customers have successfully prompted the model to bypass its safety constraints and generate prohibited content.
Why A is Wrong: Model skewing refers to attacks or biases that cause a model to favor certain outputs or perspectives systematically over time, often through data manipulation. It describes a gradual distortion of model behavior, not a direct user-prompted bypass of safety restrictions in a single interaction.
Why B is Wrong: Model theft involves extracting or replicating a proprietary model ' s functionality or architecture through repeated queries. It is an intellectual property attack aimed at stealing the model ' s knowledge, not an attack that causes the model to produce offensive content.
Why D is Wrong: Insecure output handling occurs when an application fails to properly validate or sanitize AI-generated outputs before using them in ways that could cause harm such as passing AI output directly to a system command or database query. It describes a developer implementation vulnerability, not the act of a user prompting a model to bypass its safety constraints.
NEW QUESTION # 123
For which of the following situations would a human-in-the-loop be most recommended?
Answer: B
NEW QUESTION # 124
An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values.
Which of the following job roles would most likely be responsible for correcting this error?
Answer: D
Explanation:
Basic Concept: In AI teams, different roles hold distinct responsibilities for specific aspects of the AI system.
Data quality issues such as incorrect formatting and missing values fall within the domain of data engineering, which is responsible for designing, building, and maintaining the data pipelines and datasets that feed AI models. CompTIA SecAI+ Study Guide covers AI team role definitions under governance and basic AI concepts.
Why C is Correct: A Data Engineer is responsible for building and maintaining the data pipelines, transformation processes, and data quality controls that supply AI models with properly formatted, complete data. Addressing incorrectly formatted data and missing values is a core data engineering responsibility, as these professionals own the ETL (Extract, Transform, Load) processes, data validation rules, and data quality frameworks that ensure the model receives clean, usable input.
Why A is Wrong: A Platform Engineer designs and maintains the computing infrastructure and platforms that host AI systems. Their focus is the technical environment and deployment infrastructure, not the quality or formatting of the data consumed by models.
Why B is Wrong: An MLOps Engineer manages the deployment, monitoring, and operational lifecycle of AI models in production. While they may detect data quality issues through monitoring, resolving data formatting and missing value problems is the responsibility of data engineers who own the data pipelines.
Why D is Wrong: An AI Architect designs the overall AI system architecture, component interactions, and technical strategy. While they define data requirements, the hands-on work of correcting data formatting errors and filling missing values belongs to the data engineering function.
NEW QUESTION # 125
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