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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
AI-Assisted Security24%- Security Operations Enhancement
  • 1. AI-driven threat detection and anomaly detection
    • 2. SOC automation and alert correlation
      - Operational Use of AI
      • 1. AI-enabled threat intelligence analysis
        • 2. Incident response acceleration
          AI Governance, Risk, and Compliance19%- AI Governance Frameworks
          • 1. NIST AI RMF concepts
            • 2. ISO/IEC AI governance alignment
              - Risk and Compliance
              • 1. Regulatory compliance for AI systems
                • 2. Ethical AI and responsible usage policies
                  Securing AI Systems40%- Adversarial Defense
                  • 1. Data poisoning mitigation
                    • 2. Model extraction and inference attack defense
                      - AI System Protection
                      • 1. Secure AI pipelines and deployment environments
                        • 2. Data protection and model security
                          Basic AI Concepts Related to Cybersecurity17%- AI Threat Landscape
                          • 1. Adversarial AI and model manipulation
                            • 2. AI-driven cyber threats (phishing, malware automation)
                              - AI and Machine Learning Fundamentals
                              • 1. Supervised, unsupervised, reinforcement learning
                                • 2. Neural networks and deep learning basics
                                  - Generative AI Concepts
                                  • 1. LLMs and generative AI basics
                                    • 2. Prompting and AI interaction fundamentals

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                                      CompTIA SecAI+ Certification Exam Sample Questions (Q138-Q143):

                                      NEW QUESTION # 138
                                      Which of the following technologies is used in deepfake?

                                      Answer: C

                                      Explanation:
                                      Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.


                                      NEW QUESTION # 139
                                      Users report that the output of a generative AI application seems unrelated to the prompts and contains offensive content. A security team investigates and determines that there was an on- path attack. Which of the following is the most likely attack method?

                                      Answer: D

                                      Explanation:
                                      In an on-path attack, an adversary intercepts and manipulates traffic between the user and the AI system. Session hijacking allows the attacker to inject or alter prompts and responses, leading to unrelated or offensive output.


                                      NEW QUESTION # 140
                                      An organization is concerned with the exposure of sensitive data.
                                      Which of the following is the most relevant security concern?

                                      Answer: C

                                      Explanation:
                                      Basic Concept: AI models can inadvertently memorize sensitive information from their training data. Certain attack techniques can exploit this memorization to extract private information from a deployed model, even without direct access to the training dataset. CompTIA SecAI+ Study Guide covers model inversion as an AI- specific data exposure attack vector.
                                      Why B is Correct: Model inversion is an attack where an adversary queries a deployed AI model with carefully crafted inputs to reconstruct or infer sensitive training data. For example, an attacker could query a facial recognition model with optimized images to reconstruct faces of individuals from the training set, or query a medical diagnosis model to infer patient records used in training. This directly exposes sensitive data that was supposed to be protected.
                                      Why A is Wrong: Overfitting is a model training quality issue where a model learns training data too specifically and performs poorly on new data. While it can indicate that sensitive data was memorized, overfitting itself is a performance concern rather than directly a data exposure attack vector.
                                      Why C is Wrong: Data normalization is a preprocessing technique that scales numerical features to a common range to improve training performance. It is a data preparation step with no direct relevance to sensitive data exposure or privacy attacks.
                                      Why D is Wrong: Hyperparameter tuning adjusts configuration parameters of a model to optimize its performance during training. It is an optimization technique with no relevance to protecting against sensitive data exposure attacks.


                                      NEW QUESTION # 141
                                      Developers introduce new features to their generative AI product in an effort to stand out from the competition and offer more value to customers.
                                      Which of the following most accurately explains the risks when enabling more functionality?

                                      Answer: C

                                      Explanation:
                                      Basic Concept: The relationship between AI system capabilities and security risk is a fundamental concept in AI governance. As AI models gain more functionality and capabilities, their potential for misuse, unintended consequences, and attack surface expansion grows proportionally. CompTIA SecAI+ Study Guide addresses capability-risk proportionality under AI governance.
                                      Why D is Correct: The risks of a generative AI product are proportional to its capabilities. Each new feature expands what the model can do, which simultaneously expands what adversaries can manipulate it to do, what sensitive operations it can be directed to perform, and what unintended harm it can cause. A model that can generate text, images, execute code, and call external APIs has dramatically greater risk potential than one that can only generate text. Risk grows with capability scope.
                                      Why A is Wrong: Risks do not remain constant when new features are added. New features introduce new attack vectors, expand the model ' s action space, and create new opportunities for misuse. Each addition fundamentally changes the system ' s risk profile.
                                      Why B is Wrong: While risks do increase with new features, saying they simply increase does not capture the precise relationship. The increase is proportional to the nature and scope of the capabilities added, not a uniform increment for any feature addition.
                                      Why C is Wrong: While risks can be measured qualitatively, stating that risks are measured qualitatively is a statement about measurement methodology rather than an explanation of how risks change when functionality is enabled. It does not accurately describe the relationship between capability and risk.


                                      NEW QUESTION # 142
                                      Which IR document defines who must be contacted during a breach and within what timeframe?

                                      Answer: C

                                      Explanation:
                                      The communications plan specifies notification roles and timelines.


                                      NEW QUESTION # 143
                                      ......

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