無料でゲット！2026年最新のに更新されたSISA CSPAI試験問題と解答
CSPAI問題集PDFとテストエンジン試験問題
SISA CSPAI 認定試験の出題範囲：
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質問 # 29
In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?
- A. The marketing materials associated with the AI product
- B. The user interface of the AI application
- C. The underlying ML model and its training data.
- D. The physical hardware running the AI system
正解：C
解説：
Supply chain attacks in ML exploit vulnerabilities in the ecosystem, with the core ML model and training data being prime targets due to their foundational role in system behavior. Attackers might inject backdoors into pretrained models via compromised libraries (e.g., PyTorch or TensorFlow packages) or poison datasets during sourcing, leading to manipulated outputs or data exfiltration. This is more critical than targeting UI or hardware, as model/data compromises persist across deployments, enabling stealthy, long-term exploits like trojan attacks. Mitigation includes verifying model provenance, using secure repositories, and conducting integrity checks with hashing or digital signatures. In SISA guidelines, emphasis is on end-to-end supply chain auditing to prevent such intrusions, which could result in biased decisions or security breaches in applications like recommendation systems. Protecting these components ensures model reliability and data confidentiality, integral to AI security posture. Exact extract: "In supply chain attacks on machine learning, attackers critically target the underlying ML model and its training data to introduce persistent vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risks in AI, Page 145-148).
質問 # 30
What metric is often used in GenAI risk models to evaluate bias?
- A. Number of parameters in the model.
- B. Fairness metrics like demographic parity or equalized odds.
- C. Accuracy rate without considering demographics.
- D. Computational efficiency during training.
正解：B
解説：
Bias assessment in GenAI employs fairness metrics such as demographic parity (equal outcomes across groups) or equalized odds (balanced error rates), quantifying disparities in outputs. These metrics guide debiasing techniques, ensuring ethical AI under risk models. In applications like hiring tools, they prevent discriminatory generations, aligning with regulatory requirements. Exact extract: "Fairness metrics like demographic parity are used in GenAI risk models to evaluate and mitigate bias." (Reference: Cyber Security for AI by SISA Study Guide, Section on Bias Assessment Metrics, Page 245-248).
質問 # 31
Which of the following describes the scenario where an LLM is embedded 'As-is' into an application frame?
- A. Customizing the LLM to fit specific application requirements and workflows before integration.
- B. Integrating the LLM into the application without modifications, using its out-of-the-box capabilities directly within the application.
- C. Replacing the LLM with a more specialized model tailored to the application's needs.
- D. Using the LLM solely for backend data processing, while the application handles all user interactions.
正解：B
解説：
Embedding an LLM 'as-is' means direct integration of the pretrained model into the app framework without alterations, relying on its inherent capabilities for tasks like text generation, simplifying SDLC by avoiding customization overhead. This is suitable for general-purpose apps but may lack optimization for specifics, contrasting with tailored approaches. It accelerates deployment while posing risks like unmitigated biases, necessitating post-integration safeguards. Exact extract: "It describes integrating the LLM without modifications, using out-of-the-box capabilities directly in the application." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Integration Methods, Page 110-113).
質問 # 32
Which framework is commonly used to assess risks in Generative AI systems according to NIST?
- A. The AI Risk Management Framework (AI RMF) for evaluating trustworthiness.
- B. A general IT risk assessment without AI-specific considerations.
- C. Using outdated models from traditional software risk assessment.
- D. Focusing solely on financial risks associated with AI deployment.
正解：A
解説：
The NIST AI Risk Management Framework (AI RMF) provides a structured approach to identify, assess, and mitigate risks in GenAI, emphasizing trustworthiness attributes like safety, fairness, and explainability. It categorizes risks into governance, mapping, measurement, and management phases, tailored for AI lifecycles.
For GenAI, it addresses unique risks such as hallucinations or bias amplification. Organizations apply it to conduct impact assessments and implement controls, ensuring compliance and ethical deployment. Exact extract: "NIST's AI RMF is commonly used to assess risks in Generative AI, focusing on trustworthiness and lifecycle management." (Reference: Cyber Security for AI by SISA Study Guide, Section on NIST Frameworks for AI Risk, Page 230-233).
質問 # 33
When dealing with the risk of data leakage in LLMs, which of the following actions is most effective in mitigating this issue?
- A. Allowing unrestricted access to training data.
- B. Using larger datasets to overshadow sensitive information.
- C. Applying rigorous access controls and anonymization techniques to training data.
- D. Relying solely on model obfuscation techniques
正解：C
解説：
Data leakage in LLMs occurs when sensitive information from training data is inadvertently revealed in outputs, posing privacy risks. Effective mitigation involves strict access controls, such as role-based permissions, and anonymization methods like differential privacy or tokenization to obscure personal data.
These measures prevent extraction attacks while maintaining model utility. Regular audits and data minimization further strengthen defenses. Unlike obfuscation alone, which may not fully protect, combined controls ensure compliance with regulations like GDPR. Exact extract: "Applying rigorous access controls and anonymization techniques to training data is most effective in mitigating data leakage risks in LLMs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Security in AI Models, Page 130-
133).
質問 # 34
In a Retrieval-Augmented Generation (RAG) system, which key step is crucial for ensuring that the generated response is contextually accurate and relevant to the user's question?
- A. Leveraging a diverse set of data sources to enrich the response with varied perspectives
- B. Integrating advanced search algorithms to ensure the retrieval of highly relevant documents for context.
- C. Utilizing feedback mechanisms to continuously improve the relevance of responses based on user interactions.
- D. Retrieving relevant information from the vector database before generating a response
正解：D
解説：
In RAG systems, retrieving relevant information from a vector database before generation is pivotal, as it grounds responses in verified, contextually aligned data. Using embeddings and similarity metrics, the system fetches documents matching the query's intent, ensuring accuracy and relevance. While diverse sources or feedback aid long-term improvement, the retrieval step directly drives contextual fidelity, streamlining SDLC by modularizing data access. Exact extract: "Retrieving relevant information from the vector database is crucial for ensuring contextually accurate responses in RAG systems." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Optimization, Page 120-123).
質問 # 35
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?
- A. Fine-tuning the generative model with synthetic datasets generated from the retrieved documents
- B. Tuning the retrieval model to prioritize documents with the highest semantic similarity
- C. Utilising an ensemble of multiple LLMs to cross-check the generated outputs.
- D. Implementing a redundancy check by comparing the outputs from different retrieval modules.
正解：B
解説：
The Retrieval-Augmented Generation (RAG) framework enhances generative models by incorporating external knowledge retrieval to ground outputs in factual data, thereby improving consistency and reducing hallucinations. The critical factor lies in optimizing the retrieval component to select documents with maximal semantic relevance, often using techniques like dense vector embeddings (e.g., via BERT or similar encoders) and similarity metrics such as cosine similarity. This ensures that the generator receives contextually precise information, minimizing irrelevant or misleading inputs that could lead to inconsistent outputs. For instance, in question-answering systems, prioritizing high-similarity documents allows the model to reference verified sources directly, boosting accuracy. Other approaches, like ensembles or redundancy checks, are supplementary but less foundational than effective retrieval tuning, which directly impacts the quality of augmented context. In SDLC, integrating RAG with fine-tuned retrieval accelerates development cycles by enabling modular updates without full model retraining. Security benefits include tracing outputs to sources for auditability, aligning with responsible AI practices. This method scales well for large knowledge bases, making it essential for production-grade applications where factual integrity is paramount. Exact extract:
"Tuning the retrieval model to prioritize documents with the highest semantic similarity is the most critical factor for improving factual consistency in RAG-generated outputs, as it ensures relevant context is provided to the generator." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Frameworks in SDLC Efficiency, Page 95-98).
質問 # 36
How does ISO 27563 support privacy in AI systems?
- A. By limiting AI to non-personal data only.
- B. By mandating the use of specific encryption algorithms.
- C. By focusing on performance metrics over privacy.
- D. By providing guidelines for privacy-enhancing technologies in AI.
正解：D
解説：
ISO 27563 offers practical guidance on implementing privacy-enhancing technologies (PETs) in AI, such as differential privacy or federated learning, to protect data while maintaining utility. It addresses risks like inference attacks, ensuring compliance with privacy regulations. Exact extract: "ISO 27563 supports privacy in AI by providing guidelines for privacy-enhancing technologies." (Reference: Cyber Security for AI by SISA Study Guide, Section on ISO 27563 for Privacy, Page 265-268).
質問 # 37
What is a potential risk of LLM plugin compromise?
- A. Improved model accuracy
- B. Reduced model training time
- C. Unauthorized access to sensitive information through compromised plugins
- D. Better integration with third-party tools
正解：C
解説：
LLM plugin compromises occur when extensions or integrations, like API-connected tools in systems such as ChatGPT plugins, are exploited, leading to unauthorized data access or injection attacks. Attackers might hijack plugins to leak user queries, training data, or system prompts, breaching privacy and enabling further escalations like lateral movement in networks. This risk is amplified in open ecosystems where plugins handle sensitive operations, necessitating vetting, sandboxing, and encryption. Unlike benefits like accuracy gains, compromises erode trust and invite regulatory penalties. Mitigation strategies include regular vulnerability scans, least-privilege access, and monitoring for anomalous plugin behavior. In AI security, this highlights the need for robust plugin architectures to prevent cascade failures. Exact extract: "A potential risk of LLM plugin compromise is unauthorized access to sensitive information, which can lead to data breaches and privacy violations." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security in LLMs, Page 155-158).
質問 # 38
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain.
What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?
- A. Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.
- B. Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.
- C. Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.
- D. Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.
正解：C
解説：
Single-task fine-tuning specializes the LLM but risks overfitting, limiting generalization to novel tasks unlike multi-task approaches that promote transfer learning across domains. This challenge requires careful regularization in SDLC to balance specificity and versatility, often needing more resources for version management. Exact extract: "Single-task fine-tuning is less effective in generalizing to new tasks compared to multi-task fine-tuning." (Reference: Cyber Security for AI by SISA Study Guide, Section on Fine-Tuning Challenges, Page 115-118).
質問 # 39
How does the STRIDE model adapt to assessing threats in GenAI?
- A. By using it unchanged from traditional software.
- B. By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to AI components.
- C. By focusing only on hardware threats in AI systems.
- D. By excluding AI-specific threats like model inversion.
正解：B
解説：
The STRIDE model adapts to GenAI by evaluating threats across its categories: Spoofing (e.g., fake inputs), Tampering (e.g., data poisoning), Repudiation (e.g., untraceable generations), Information Disclosure (e.g., leakage from prompts), Denial of Service (e.g., resource exhaustion), and Elevation of Privilege (e.g., jailbreaking). This systematic threat modeling helps in designing resilient GenAI systems, incorporating AI- unique aspects like adversarial inputs. Exact extract: "STRIDE adapts to GenAI by applying its threat categories to AI components, assessing specific risks like tampering or disclosure." (Reference: Cyber Security for AI by SISA Study Guide, Section on Threat Modeling for GenAI, Page 240-243).
質問 # 40
Which of the following is a characteristic of domain-specific Generative AI models?
- A. They are only used for computer vision tasks
- B. They are tailored and fine-tuned for specific fields or industries
- C. They are designed to run exclusively on quantum computers
- D. They are trained on broad datasets covering multiple domains
正解：B
解説：
Domain-specific Generative AI models are refined versions of foundational models, adapted through fine- tuning on specialized datasets to excel in niche areas like healthcare, finance, or legal applications. This tailoring enhances precision, relevance, and efficiency by incorporating industry-specific jargon, patterns, and constraints, unlike general models that handle broad tasks but may lack depth. For example, a medical GenAI model might generate accurate diagnostic reports by focusing on clinical data, reducing errors in specialized contexts. This approach balances computational resources and performance, making them ideal for targeted deployments while maintaining the generative capabilities of larger models. Security implications include better control over sensitive domain data. Exact extract: "Domain-specific GenAI models are characterized by being tailored and fine-tuned for particular fields or industries, leveraging specialized data to achieve higher accuracy and relevance in those domains." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Model Types, Page 65-67).
質問 # 41
In a scenario where Open-Source LLMs are being used to create a virtual assistant, what would be the most effective way to ensure the assistant is continuously improving its interactions without constant retraining?
- A. Reducing the amount of feedback integrated to speed up deployment.
- B. Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.
- C. Training a larger proprietary model to replace the open-source LLM
- D. Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.
正解：B
解説：
For continuous improvement in open-source LLM-based virtual assistants, RLHF integrates human evaluations to align model outputs with preferences, iteratively refining behavior without full retraining. This method uses reward models trained on feedback to guide policy optimization, enhancing interaction quality over time. It addresses limitations like initial biases or suboptimal responses by leveraging real-world user inputs, making the system adaptive and efficient. Unlike full retraining, RLHF is parameter-efficient and scalable, ideal for production environments. Security benefits include monitoring feedback for adversarial attempts. Exact extract: "Implementing RLHF allows continuous refinement of the assistant's interactions based on user feedback, avoiding the need for constant full retraining while improving performance." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Improvement Techniques in SDLC, Page 85-88).
質問 # 42
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?
- A. Freezing the majority of model parameters and only updating a small subset relevant to the task
- B. Using external reinforcement learning to adjust the model's parameters dynamically.
- C. Training the model from scratch on the target task to achieve optimal performance.
- D. Implementing multiple independent models for each specific task instead of fine tuning a single model
正解：A
解説：
Parameter-efficient fine-tuning (PEFT) strategies, like LoRA or adapters, freeze most pretrained parameters and train only lightweight modules, reducing computational costs while adapting to new tasks. This preserves general knowledge, prevents catastrophic forgetting, and enables quick deployments in resource-constrained settings. For LLMs, it's crucial for efficiency in production, allowing specialization without retraining billions of parameters. Security-wise, it minimizes exposure to new data risks. Exact extract: "A common strategy is freezing the majority of model parameters and updating only a small task-relevant subset, ensuring efficiency in fine-tuning for production deployment." (Reference: Cyber Security for AI by SISA Study Guide, Section on Efficient Fine-Tuning in SDLC, Page 90-92).
質問 # 43
In line with the US Executive Order on AI, a company's AI application has encountered a security vulnerability. What should be prioritized to align with the order's expectations?
- A. Immediate public disclosure of the vulnerability.
- B. Ignoring the vulnerability if it does not affect core functionalities.
- C. Halting all AI projects until a full investigation is complete.
- D. Implementing a rapid response to address and remediate the vulnerability, followed by a review of security practices.
正解：D
解説：
The US Executive Order on AI emphasizes proactive risk management and robust security to ensure safe AI deployment. When a vulnerability is detected, rapid response to remediate it, coupled with a thorough review of security practices, aligns with these mandates by minimizing harm and preventing recurrence. This approach involves patching the issue, assessing root causes, and updating protocols to strengthen defenses, ensuring compliance with standards like ISO 42001, which prioritizes risk mitigation in AI systems. Public disclosure, while important, is secondary to remediation to avoid premature exposure, and halting projects is overly disruptive unless risks are critical. Ignoring vulnerabilities contradicts responsible AI principles, risking regulatory penalties and trust erosion. This strategy fosters accountability and aligns with governance frameworks for secure AI operations. Exact extract: "Addressing vulnerabilities promptly through remediation and reviewing security practices is prioritized to meet the US Executive Order's expectations for safe and secure AI systems." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Governance and US EO Compliance, Page 165-168).
質問 # 44
How does GenAI contribute to incident response in cybersecurity?
- A. By automating playbook generation and response orchestration.
- B. By delaying responses to gather more data for analysis.
- C. By focusing only on post-incident reporting.
- D. By manually reviewing each incident without AI assistance.
正解：A
解説：
GenAI enhances incident response by dynamically generating customized playbooks based on threat intelligence and orchestrating automated actions like isolation or patching. It processes vast logs in real-time, correlating events to prioritize alerts and suggest optimal responses, reducing mean time to respond (MTTR).
For complex incidents, it simulates outcomes of different strategies, aiding decision-making. This automation frees analysts for strategic tasks, improving efficiency and effectiveness in containing breaches. Exact extract:
"GenAI contributes to incident response by automating playbook generation and orchestration, enhancing cybersecurity operations." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Incident Response, Page 215-218).
質問 # 45
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?
- A. Enabling real-time detection of vulnerabilities with actionable insights.
- B. Limiting its use to only high-priority vulnerabilities.
- C. Reducing the need for manual vulnerability assessment entirely
- D. Automatically patching vulnerabilities without additional configuration
正解：A
解説：
Giskard, an open-source tool, enhances AI security by enabling real-time vulnerability detection, scanning models for issues like bias or adversarial weaknesses, and providing actionable insights for remediation. This proactive approach supports continuous monitoring, unlike automated patching or limited scopes, and integrates into SDLC for robust security. Exact extract: "Giskard enables real-time detection of vulnerabilities with actionable insights, strengthening AI security functions." (Reference: Cyber Security for AI by SISA Study Guide, Section on Vulnerability Detection Tools, Page 190-193).
質問 # 46
In a Transformer model processing a sequence of text for a translation task, how does incorporating positional encoding impact the model's ability to generate accurate translations?
- A. It ensures that the model treats all words as equally important, regardless of their position in the sequence.
- B. It simplifies the model's computations by merging all words into a single representation, regardless of their order
- C. It helps the model distinguish the order of words in the sentence, leading to more accurate translation by maintaining the context of each word's position.
- D. It speeds up processing by reducing the number of tokens the model needs to handle.
正解：C
解説：
Positional encoding in Transformers addresses the lack of inherent sequential information in self-attention by embedding word order into token representations, using functions like sine and cosine to assign unique positional vectors. This enables the model to differentiate word positions, crucial for translation where syntax and context depend on sequence (e.g., subject-verb-object order). Without it, Transformers treat inputs as bags of words, losing syntactic accuracy. Positional encoding ensures precise contextual understanding, unlike options that misrepresent its role. Exact extract: "Positional encoding helps Transformers distinguish word order, leading to more accurate translations by maintaining positional context." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer Components, Page 55-57).
質問 # 47
How can Generative AI be utilized to enhance threat detection in cybersecurity operations?
- A. By creating synthetic attack scenarios for training detection models.
- B. By generating random data to overload security systems.
- C. By replacing all human analysts with AI-generated reports.
- D. By automating the deletion of security logs to reduce storage costs.
正解：A
解説：
Generative AI improves security posture by synthesizing realistic cyber threat scenarios, which can be used to train and test detection systems without exposing real networks to risks. This approach allows for the creation of diverse, evolving attack patterns that mimic advanced persistent threats, enabling machine learning models to learn from simulated data and improve accuracy in identifying anomalies. For example, GenAI can generate phishing emails or malware variants, helping in proactive defense tuning. This not only enhances detection rates but also reduces false positives through better model robustness. Integration into security operations centers (SOCs) facilitates continuous improvement, aligning with zero-trust architectures. Security benefits include cost-effective training and faster response to emerging threats. Exact extract: "Generative AI enhances threat detection by creating synthetic attack scenarios for training models, thereby improving the overall security posture without real-world risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Applications in Threat Detection, Page 200-203).
質問 # 48
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検証済みのCSPAIテスト問題集と解答で正確な52問題と解答あります：https://www.goshiken.com/SISA/CSPAI-mondaishu.html
最新をゲットせよ！CSPAI認定有効な試験問題集解答：https://drive.google.com/open?id=1jk2SuUJ2szuNFHqVVGMqwqpx47CSoX4y