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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - Core AI principles and terminology
|
| AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
| Securing AI Systems | 40% | - Secure AI development and operations
|
| AI-assisted Security | 24% | - Security automation and orchestration
|
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NEW QUESTION # 84
Global IPs attempt logins against thousands of accounts with known breached credentials. What attack is occurring?
Answer: B
Explanation:
Credential stuffing uses leaked username-password pairs.
NEW QUESTION # 85
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department's AI plagiarism detection service.
Which of the following principles will most likely to address their concerns?
Answer: A
Explanation:
Consistency ensures that an AI system applies rules and produces results in a uniform manner across all cases. This principle directly addresses concerns about bias and irregular outcomes in the plagiarism detection service.
NEW QUESTION # 86
Which of the following is a risk addressed by responsible AI?
Answer: A
Explanation:
Basic Concept: Responsible AI is a governance framework addressing risks that arise from AI systems producing outcomes that are unfair, harmful, or contrary to human values. Different risk types fall under different governance domains - some under responsible AI, others under security or operational management. CompTIA SecAI+ Study Guide covers responsible AI risk categories under Domain 4.
Why C is Correct: Response bias occurs when an AI system ' s outputs are systematically skewed against certain groups, topics, or perspectives, reflecting biases embedded in training data or model design. This is a core risk addressed by responsible AI principles including fairness, non-discrimination, and explainability.
Responsible AI frameworks mandate bias detection, assessment, and mitigation to ensure AI responses treat all users and groups equitably.
Why A is Wrong: Model drift describes the degradation of model performance over time as the distribution of real-world data diverges from the training data distribution. While an important operational concern, model drift is primarily a technical performance risk managed through MLOps and monitoring practices, not a core responsible AI governance concern.
Why B is Wrong: Reputational loss is a business risk consequence that may result from various AI failures including biased outputs or privacy violations. It is an outcome or impact rather than a specific risk category that responsible AI frameworks directly address.
Why D is Wrong: Data poisoning is a security attack where adversaries corrupt AI training data to manipulate model behavior. This is a cybersecurity threat managed through security controls and data integrity protections rather than responsible AI ethical governance frameworks focused on fairness and accountability.
NEW QUESTION # 87
A cybersecurity administrator generates patching reports using AI, but the process takes a long time. Which of the following is the best way to increase performance?
Answer: D
Explanation:
Basic Concept: AI systems that repeatedly query external data sources for similar information during a single report generation process spend significant time on redundant network requests. Caching frequently accessed data locally eliminates this overhead. CompTIA SecAI+ Study Guide covers AI performance optimization strategies in security operations contexts.
Why B is Correct: Downloading the full CVE database locally before starting the cross-referencing process eliminates the need for multiple individual external API calls as the AI processes each OS version ' s patch list. Instead of making thousands of small external queries to look up CVE information for each patch-OS combination, the AI can query the locally cached database internally. This transforms multiple slow external network operations into fast local lookups, dramatically reducing report generation time.
Why A is Wrong: Using an MCP server to run multiple LLM queries simultaneously could improve throughput through parallelization. However, the fundamental bottleneck is external CVE database queries, not LLM processing capacity. Parallelizing LLM calls does not eliminate the external query latency.
Why C is Wrong: Specifying summarization algorithms in the system prompt affects how the AI structures its output. It does not address the time-consuming external data retrieval process that is the actual performance bottleneck in this cross-referencing workflow.
Why D is Wrong: Increasing token limits prevents session restarts for long contexts but does not address the external query latency that makes the report slow to generate. The bottleneck is data retrieval speed, not token limit constraints causing session breaks.
NEW QUESTION # 88
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: A,C
Explanation:
Basic Concept: Prompting techniques for LLMs include various approaches to guide model behavior.
Providing examples within prompts is a powerful technique that leverages the model ' s in-context learning capability to guide response format and quality. CompTIA SecAI+ Study Guide covers prompting techniques under basic AI concepts.
Why E is Correct: One-shot prompting involves providing exactly one example within a prompt to demonstrate to the model the desired input-output format or response style. This single example guides the model ' s understanding of the task without requiring extensive fine-tuning. It is a well-established prompting technique that uses examples to inform model behavior.
Why F is Correct: Multi-shot prompting (also called few-shot prompting) involves providing multiple examples within a prompt to further clarify the desired output pattern. Multiple examples help the model identify consistent patterns and produce more accurate, consistent responses. Both one-shot and multi-shot are specifically defined by their use of examples in prompts.
Why A is Wrong: A user prompt is the input message submitted by a user to the AI system. It is the general term for any user input, not a specific technique that describes the practice of providing examples.
Why B is Wrong: A system prompt sets the model ' s behavior, persona, and constraints at the session level.
While a system prompt could contain examples, the term specifically refers to the system-level instruction context, not the technique of example provision.
Why C is Wrong: A prompt template is a reusable structured format with placeholders for variable inputs. It standardizes prompt structure but is not defined by the practice of including examples.
Why D is Wrong: Quantization is a model compression technique that reduces model size by representing weights with lower precision numbers. It is a model optimization technique completely unrelated to prompting practices.
NEW QUESTION # 89
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