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| Section | Weight | Objectives |
|---|---|---|
| AI-assisted Security | 24% | - AI in security strategy and operations
|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Securing AI Systems | 40% | - Security controls for AI systems
|
| Basic AI Concepts Related to Cybersecurity | 17% | - Core AI principles and terminology
|
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NEW QUESTION # 136
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Answer: A
Explanation:
Basic Concept: Reconnaissance is the information gathering phase of an attack. LLMs can be enhanced to perform more effective reconnaissance by giving them access to current, specific information beyond their training data cutoff. CompTIA SecAI+ covers AI augmentation techniques including RAG under AI-assisted security.
Why A is Correct: RAG enhances an LLM by connecting it to an external knowledge base or real-time data sources that it can query during inference. For reconnaissance purposes, a RAG-enabled LLM can access up- to-date organizational information, technical documentation, and intelligence feeds that go beyond its static training data. This makes the LLM significantly more capable for gathering current, targeted intelligence about specific organizations or infrastructure.
Why B is Wrong: Creating a web scraper is a basic data collection technique. While AI can help write scraper code, the scraper itself is a simple script that does not enhance the LLM ' s intelligence or reasoning capabilities for sophisticated reconnaissance.
Why C is Wrong: Instructing an AI assistant to query as an administrator is a prompt manipulation attempt.
An LLM cannot actually gain elevated permissions through a prompt instruction; this describes social engineering or privilege escalation via prompting, not a performance enhancement technique.
Why D is Wrong: Prompting a chatbot to describe naming patterns is a basic use of an existing LLM ' s knowledge. It does not strengthen or enhance the model ' s capabilities; it merely queries what the model already knows from training data, which may be outdated or generic.
NEW QUESTION # 137
Which of the following technologies is used in deepfake?
Answer: B
Explanation:
Basic Concept: Deepfakes are AI-generated synthetic media that convincingly replace or manipulate a person
' s likeness, voice, or actions in images and videos. Creating realistic deepfakes requires generative AI techniques capable of learning and reproducing complex data distributions. CompTIA SecAI+ Exam Objectives cover deepfake technology under basic AI concepts.
Why A is Correct: Generative Adversarial Networks (GANs) are the primary technology behind deepfakes. A GAN consists of two competing neural networks: a generator that creates synthetic content and a discriminator that evaluates whether content is real or fake. Through adversarial training, the generator continuously improves at creating convincing synthetic media such as realistic human faces, voice clones, and video manipulations indistinguishable from authentic recordings.
Why B is Wrong: Multi-shot prompting is a prompting technique where multiple examples are provided to an LLM to guide its responses. It is an inference technique for language models and has no role in generating synthetic video or image deepfake content.
Why C is Wrong: Prompt engineering is the practice of crafting effective prompts to guide LLM outputs. It is a communication strategy for working with text-based AI systems, not a technology for generating synthetic media.
Why D is Wrong: Transfer learning is a training technique that repurposes knowledge from one domain to another, improving model performance with limited data. While it can be used in model training pipelines, it is not the core technology that enables deepfake generation.
NEW QUESTION # 138
A loan company compares a machine learning model that was found on the dark web with the company ' s proprietary model. Using proprietary information, the company generates the following:
Input
Dark web model
Company ' s proprietary model
Jane A.
Approve
Approve
John B.
Deny
Deny
Ann J.
Deny
Deny
Joe H.
Approve
Approve
Which of the following should the AI red team recommend to mitigate risks for future deployments?
Answer: B
Explanation:
The matching behavior between the unauthorized dark-web model and the company ' s proprietary model strongly indicates a model-theft or unauthorized model-copying risk. Model encryption is the most appropriate control among the available choices because it directly protects proprietary model artifacts, including model weights and other sensitive intellectual property, from unauthorized access or extraction when stored or transferred. CompTIA SecAI+ specifically identifies model theft as an AI-system attack and lists encryption among the compensating controls used to protect AI systems. Parameter anonymization is not the primary protection for proprietary model weights and does not prevent unauthorized possession of the model itself. A prompt firewall analyzes prompts for malicious or prohibited input but does not protect model files from theft. Input validation similarly protects an application from malformed or malicious inputs rather than safeguarding the model ' s intellectual property. Therefore, strengthening protection of the model artifact through encryption is the most directly applicable mitigation for future deployments.
NEW QUESTION # 139
Which of the following controls is the best way to mitigate a denial-of-service (DoS) attack?
Answer: D
Explanation:
Rate limiting restricts the number of requests within a set timeframe, preventing attackers from overwhelming the system with excessive traffic, making it the best control to mitigate a DoS attack.
NEW QUESTION # 140
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 # 141
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