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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks |
| Topic 2: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure systems design |
| Topic 3: Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security |
| Topic 4: Architecture and Design | 21% | - Explain the importance of physical security controls - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts |
| Topic 5: Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
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NEW QUESTION # 55
Which of the following is the primary purpose of validating data for an AI system?
Answer: D
Explanation:
Validating data ensures quality, consistency, and fairness in training sets, helping prevent biased or inaccurate results in AI system outputs.
NEW QUESTION # 56
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: B
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 57
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: B
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 # 58
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: A
NEW QUESTION # 59
A line of business wants to onboard an application that uses a custom AI model for employee assessments.
The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model.
Which of the following is the most appropriate to use for an AI threat model?
Answer: C
Explanation:
Basic Concept: Threat modeling for AI systems requires a framework specifically designed to address AI- specific attack techniques, tactics, and procedures. General cybersecurity or governance frameworks do not capture the unique adversarial attack surface of AI and ML systems. CompTIA SecAI+ Exam Objectives identify MITRE ATLAS as the primary AI threat modeling resource.
Why B is Correct: MITRE ATLAS (Adversarial Threat Landscape for AI Systems) is specifically designed as an AI and ML threat modeling framework. It catalogs real-world adversarial tactics, techniques, and procedures targeting AI systems, enabling security architects to identify and assess threats unique to ML models such as data poisoning, model extraction, and evasion attacks. It is the industry standard for AI- specific threat modeling.
Why A is Wrong: Responsible AI is a set of ethical principles and governance guidelines for developing and deploying AI systems fairly and safely. It addresses ethics and fairness, not technical adversarial threat modeling.
Why C is Wrong: The OECD provides non-binding policy recommendations and principles for AI governance at an international level. It does not provide technical threat modeling taxonomies or AI-specific attack catalogs.
Why D is Wrong: ISO standards such as ISO 42001 establish management system requirements for AI governance. They are compliance and management frameworks, not threat modeling tools for identifying adversarial AI attack vectors.
NEW QUESTION # 60
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