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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 2: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 3: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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NEW QUESTION # 39
Customer feedback for an AI chatbot has a high-rate of non-answers, which is causing higher central processing unit (CPU) utilization.
Which of the following should be implemented?
Answer: B
Explanation:
Basic Concept: AI chatbots that generate non-answers - responses that do not actually address user questions
- consume CPU resources for processing without delivering value. This can indicate the model is attempting to generate responses for queries outside its knowledge domain or confidence threshold. CompTIA SecAI+ Study Guide covers AI performance optimization and response quality management.
Why B is Correct: Implementing a response confidence level threshold allows the chatbot to recognize when it lacks sufficient confidence to provide a meaningful answer and respond accordingly, either with a helpful redirect or a clear indication that it cannot answer the query. This reduces the costly processing cycles spent generating poor-quality non-answers, lowers CPU utilization from failed response generation, and improves customer experience by setting appropriate expectations rather than returning unhelpful responses.
Why A is Wrong: Guardrails filter content for safety and policy compliance. They prevent harmful or out-of- policy responses but do not address the underlying issue of the model generating low-confidence non-answers to legitimate customer queries.
Why C is Wrong: Prompt logging records user inputs for analysis and auditing. While useful for identifying what types of questions cause non-answers, logging alone does not solve the problem or reduce CPU utilization from failed response generation.
Why D is Wrong: Cost monitoring tracks AI system expenditure. It can identify that costs are high due to excessive CPU usage but does not implement a solution to reduce the non-answer rate or improve response generation efficiency.
NEW QUESTION # 40
A customer-facing, AI-powered chatbot has been jailbroken through prompt injections. As a result, the AI model is offering a 99% discount on the purchase of a new vehicle. Which of the following should be implemented to enhance the model's robustness against such attacks?
Answer: D
Explanation:
Guardrails enforce strict rules on what the AI model can and cannot do, preventing malicious prompt injections from overriding intended behavior. In this case, guardrails would stop the chatbot from generating unauthorized offers such as extreme discounts.
NEW QUESTION # 41
An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack. Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?
Answer: D
Explanation:
MITRE ATLAS is specifically designed to capture adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems. It helps organizations assess both the probability and impact of AI/ML-related attacks, making it the most relevant threat-modeling resource.
NEW QUESTION # 42
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: C
Explanation:
Basic Concept: Securing AI systems requires a structured, end-to-end approach that addresses security at every phase of the AI model ' s lifecycle from data collection through training, testing, deployment, and ongoing monitoring. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the foundational framework for AI system security.
Why B is Correct: A secure Model Development Life Cycle (MDLC) integrates security practices at every stage of AI model development specifically tailored to ML workflows. It encompasses secure data handling, training data validation, model testing for adversarial robustness, secure deployment practices, and ongoing monitoring. Unlike generic software development lifecycles, the MDLC addresses ML-specific risks such as data poisoning, model drift, and adversarial attacks.
Why A is Wrong: Guardrail testing and security validation are important components of the MDLC but represent only the testing phase. They do not encompass the full lifecycle of security practices needed from data acquisition through production monitoring.
Why C is Wrong: Implementing comprehensive security architecture is a broad statement that describes an outcome rather than a specific actionable practice. It does not provide the structured, ML-specific guidance of an MDLC.
Why D is Wrong: A secure SDLC is designed for traditional software development and covers code security, testing, and deployment. While relevant to AI application development, it does not specifically address ML model-specific risks such as training data security, model integrity, and inference-time attacks.
NEW QUESTION # 43
A threat intelligence team wants to automate the analysis and summary of its in-depth reports for multiple target audiences. Which of the following is the best solution?
Answer: D
Explanation:
Using an LLM with retrieval-augmented generation and current data is the best solution because the requirement is to analyze substantial threat-intelligence material and generate tailored summaries for different audiences. CompTIA SecAI+ explicitly identifies document synthesis and summarization as an AI-assisted security automation use case and also includes retrieval-augmented generation as a core AI concept.
RAG enables the LLM to retrieve relevant information from an organization ' s current threat-intelligence repositories before generating its response. This improves factual grounding and allows reports to incorporate recent indicators, adversary information, vulnerabilities, campaigns, and internal findings that may not exist in the model ' s original training data. The LLM can then adapt the same underlying intelligence into executive summaries, analyst-level reports, or technical remediation guidance.
Sentiment analysis determines emotional or opinion-related characteristics and does not meet the summarization requirement. K-means clustering is useful for grouping similar observations but does not naturally generate audience-specific narrative reports. Regression predicts numerical values rather than synthesizing complex textual intelligence. Consequently, an LLM combined with RAG provides the strongest fit for automated, current, audience-aware threat-report analysis and summarization.
NEW QUESTION # 44
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