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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| Topic 2: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 4: Securing AI Systems | 40% | - Adversarial Defense
|
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NEW QUESTION # 156
An AI architect reviews AI utilization and wants to improve the user experience.
Which of the following should the architect review within the logs?
Answer: D
Explanation:
Basic Concept: User experience with AI systems is directly correlated to how accurately and relevantly the model responds to user queries. Poor model accuracy manifests as irrelevant, incorrect, or unhelpful responses, which is the primary driver of poor user experience. CompTIA SecAI+ Study Guide covers AI performance monitoring and user experience optimization.
Why B is Correct: Model accuracy metrics in logs reveal how often the model provides correct, relevant, and useful responses. Reviewing accuracy-related log data such as confidence scores, response quality ratings, error rates, and user feedback correlations enables the architect to identify performance gaps causing poor experiences and guides optimization efforts like fine-tuning or retrieval improvements.
Why A is Wrong: Rate monitoring tracks API call frequency and throughput. While important for capacity planning and detecting abuse, it does not directly reflect the quality of model responses that determine user experience.
Why C is Wrong: Access controls manage who can use the system and what permissions they have. They are a security concern rather than a user experience metric. Reviewing access control logs does not reveal information about response quality.
Why D is Wrong: Data storage metrics relate to storage capacity, utilization, and performance of data persistence layers. While these can affect response speed, they do not provide insights into model response quality or accuracy that drive user experience.
NEW QUESTION # 157
A cyberthreat intelligence (CTI) analyst conducts research about a threat during a recent attack. The analyst uses an AI threat-modeling resource to find the relevant risks and utilizes causal and domain taxonomies.
Which of the following best describes which resource the analyst is using?
Answer: D
Explanation:
The MIT AI Risk Repository is specifically characterized by its use of both a Causal Taxonomy and a Domain Taxonomy. The repository consolidates AI risks from numerous frameworks and allows practitioners to classify and research risks according to how, when, and why they occur as well as the domain of harm involved. MIT states that its Causal Taxonomy addresses factors such as the responsible entity, intent, and timing, while its Domain Taxonomy categorizes risks into broader domains and subdomains. This terminology directly matches the scenario. CompTIA SecAI+ includes the MIT AI Risk Repository among the threat-modeling resources candidates must understand. MITRE ATLAS instead organizes adversarial AI activity into tactics, techniques, procedures, and case studies. The CVE AI Working Group focuses on vulnerabilities and related vulnerability-management concerns rather than causal/domain risk taxonomies.
OWASP ' s Machine Learning Security Top 10 concentrates on prominent ML security risks and defensive awareness. Because the question explicitly identifies causal and domain taxonomies, the MIT AI Risk Repository is the precise resource being described.
NEW QUESTION # 158
A short AI-generated video shows a celebrity ' s likeness talking about a fake public security event.
Which of the following was used to create this video?
Answer: A
Explanation:
Basic Concept: Creating realistic deepfake videos that convincingly replicate a real person ' s facial expressions, movements, and voice requires deep learning models capable of learning and synthesizing complex spatial and temporal features from existing video data. CompTIA SecAI+ covers deepfake technologies under basic AI concepts.
Why B is Correct: Convolutional Neural Networks are foundational to deepfake video generation. CNNs excel at learning spatial features from visual data and are used within deepfake architectures to analyze source and target faces, extract facial features, and synthesize realistic face swaps or face animations. Modern deepfake systems typically combine CNNs with autoencoders and GANs to generate convincing video content showing a person saying or doing things they never did.
Why A is Wrong: Statistical analysis involves mathematical methods for analyzing data distributions and relationships. It does not have the capability to generate synthetic video content or replicate a person ' s visual likeness in motion.
Why C is Wrong: An ML classifier assigns input data to predefined categories. Classification models detect and label content rather than generating new synthetic video content of a person ' s likeness. They are detection tools, not generation tools.
Why D is Wrong: Random forest is an ensemble ML method using multiple decision trees for classification and regression tasks. It works on structured, tabular data and cannot process or generate visual, spatial data needed for realistic deepfake video synthesis.
NEW QUESTION # 159
A penetration tester is assessing the controls of a deployed AI system that is designed to search and return the contents of files.
The tester runs the following:
Which of the following is the best control to prevent abuse of the system?
Answer: A
Explanation:
Basic Concept: AI systems that access file systems or databases use service accounts to authenticate.
Applying the principle of least privilege to these service accounts limits the damage that can result from prompt injection or other attacks that cause the AI to perform unauthorized file access. CompTIA SecAI+ Study Guide covers least privilege as a core AI security control.
Why D is Correct: Reducing the privilege scope of the service account implements the least privilege principle, ensuring the AI system can only access files it legitimately needs for its intended function. If an attacker uses prompt injection to abuse the file search capability, the service account ' s limited permissions prevent access to sensitive files outside the defined scope, containing the blast radius of any exploitation.
Why A is Wrong: Custom detection rules identify anomalous behavior after it occurs. They are detective controls, not preventive controls. They do not stop an attacker from successfully abusing the system; they only alert after abuse has occurred.
Why B is Wrong: VPC segmentation isolates the workload at the network level, limiting lateral movement.
However, it does not restrict what files the AI ' s service account can access within its own environment, so file abuse attacks within the segment are still possible.
Why C is Wrong: LLM guardrails filter prompt inputs and outputs for policy violations. While useful, they can potentially be bypassed through sophisticated prompt injection. Reducing service account privileges provides a defense-in-depth layer that limits damage even if guardrails are bypassed.
NEW QUESTION # 160
A multinational company wants to implement an AI-assisted job screening solution.
Which of the following should the company reference to reduce the risk of incurring compliance-related fines?
Answer: A
Explanation:
Basic Concept: AI systems used in employment contexts such as job screening carry significant regulatory risk. For a multinational company operating in or serving markets covered by the EU AI Act, compliance with this binding regulation is mandatory to avoid substantial fines. CompTIA SecAI+ Exam Objectives cover AI regulatory compliance under Domain 4.
Why B is Correct: The EU AI Act explicitly classifies AI systems used for employment screening, candidate evaluation, and worker management as high-risk AI applications. These systems are subject to strict compliance requirements including mandatory conformity assessments, human oversight, transparency obligations, and registration. Non-compliance can result in fines up to 30 million euros or 6% of global annual turnover. A multinational company implementing AI job screening must reference the EU AI Act as the primary compliance obligation.
Why A is Wrong: ISO AI standards such as ISO 42001 are voluntary management system standards. While useful for best practices, they do not carry legal enforcement power and adherence does not prevent regulatory fines from binding legislation like the EU AI Act.
Why C is Wrong: Corporate policy is an internal governance document that sets organizational standards. It cannot supersede external legal obligations and following only corporate policy does not protect against fines from regulatory bodies enforcing the EU AI Act.
Why D is Wrong: NIST AI RMF is a voluntary American risk management framework. While excellent for AI risk governance, it is not a binding regulation and does not address the legal compliance requirements that generate fines from regulatory authorities in jurisdictions covered by the EU AI Act.
NEW QUESTION # 161
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