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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering attacks |
| Topic 2: Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security |
| Topic 3: Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts |
| Topic 4: Governance, Risk, and Compliance | 14% | - 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 - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies |
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NEW QUESTION # 98
User experience is declining since the launch of a large language model (LLM) in internal networks. Which of the following should be the highest priority for the prompt engineers?
Answer: D
Explanation:
When user experience is declining after an LLM launch, the top priority for prompt engineers is quality control. Ensuring prompts produce accurate, relevant, and safe outputs directly improves usability and restores user trust.
NEW QUESTION # 99
A human resources officer is using AI to evaluate resumes and help select candidates that meet minimum criteria. To improve the results, the human resources officer adjusts the query parameters and includes an example resume that matches a successful candidate. Which if the following best describes this query?
Answer: C
Explanation:
One-shot prompting provides the model with a single example (in this case, a successful resume) to guide how it should process future inputs. This technique helps the AI better align its output with the desired evaluation criteria.
NEW QUESTION # 100
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability.
Which of the following models should the analyst use?
Answer: B
Explanation:
Basic Concept: Different ML model architectures offer varying degrees of explainability. In cybersecurity, understanding why a model classified a log entry as malicious or benign is critical for analyst trust, investigation, and regulatory compliance. CompTIA SecAI+ covers model explainability under responsible AI and basic AI concepts.
Why C is Correct: Decision trees are inherently interpretable models that classify data through a series of transparent if-then rules. Every classification decision can be traced through the exact path of conditions that led to it, showing precisely which log entry features triggered the classification. Analysts can read and understand the decision path, making decision trees the gold standard for explainable ML classification in security applications where understanding the reason for a classification is as important as the classification itself.
Why A is Wrong: Large language models are complex transformer architectures with hundreds of billions of parameters. They function as black boxes - their internal decision-making processes are not human- interpretable, making them poor choices when explainability is the primary requirement.
Why B is Wrong: Neural networks are non-linear black box models. While they can achieve high classification accuracy, their multi-layer architecture makes it extremely difficult to explain why specific decisions were made in human-understandable terms.
Why D is Wrong: Generative adversarial networks are designed for generating synthetic data, not for classification tasks. They consist of competing generator and discriminator networks and are fundamentally unsuitable for log entry classification with explainability requirements.
NEW QUESTION # 101
Which of the following is most resistant to AI manipulation?
Answer: C
Explanation:
Reducing the attack surface limits the number of potential entry points that attackers or manipulated AI inputs can exploit. This proactive security measure is more resistant to AI-driven manipulation than reactive controls like payload scanning or antivirus.
NEW QUESTION # 102
A security analyst is preparing a presentation for the sales team that describes the most common vulnerabilities that are specific to AI applications.
Which of the following is the best source for the analyst to consult?
Answer: B
Explanation:
Basic Concept: Identifying AI-specific application vulnerabilities requires consulting a resource that has cataloged and documented the unique vulnerability types that affect AI systems, particularly LLMs. Different security standards serve different purposes, and selecting the right reference for AI application vulnerabilities is essential. CompTIA SecAI+ Study Guide references OWASP for AI application vulnerability guidance.
Why C is Correct: OWASP maintains the OWASP Top 10 for Large Language Model Applications, which specifically catalogs the most critical and common vulnerabilities in AI applications including prompt injection, sensitive information disclosure, excessive agency, insecure output handling, and training data poisoning. This AI-specific vulnerability list is the most directly relevant and accessible resource for a presentation on AI application vulnerabilities.
Why A is Wrong: ISO 27001 is a general information security management system standard covering broad organizational security controls. It does not specifically catalog AI application vulnerabilities or LLM-specific weakness categories.
Why B is Wrong: CWE catalogues software weakness types at a code and design level for traditional software. While some weaknesses apply to AI systems, CWE does not have a dedicated AI application vulnerability taxonomy comparable to the OWASP LLM Top 10.
Why D is Wrong: NIST RMF is a risk management framework providing guidance for managing and reducing information security risk. It is a process framework, not a vulnerability catalog, and does not list specific AI application vulnerability types suitable for a vulnerabilities presentation.
NEW QUESTION # 103
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