ハイパスレートのCSPAI資料勉強 & 合格スムーズCSPAI受験料 | 効率的なCSPAI日本語資格取得JPTestKingは専門的に IT認証試験に関する資料を提供するサイトで、１００パーセントの合格率を保証できます。それもほとんどの受験生はJPTestKingを選んだ理由です。JPTestKingはいつまでも受験生のニーズに注目していて、できるだけ皆様のニーズを満たします。 JPTestKingのSISAのCSPAI試験トレーニング資料は今までがないIT認証のトレーニング資料ですから、JPTestKingを利用したら、あなたのキャリアは順調に進むことができるようになります。 SISA Certified Security Professional in Artificial Intelligence 認定 CSPAI 試験問題 (Q15-Q20):質問 # 15
How does the multi-head self-attention mechanism improve the model's ability to learn complex relationships in data?
A. By forcing the model to focus on a single aspect of the input at a time.
B. By allowing the model to focus on different parts of the input through multiple attention heads
C. By simplifying the network by removing redundancy in attention layers.
D. By ensuring that the attention mechanism looks only at local context within the input
正解：B
解説：
Multi-head self-attention enhances a model's capacity to capture intricate patterns by dividing the attention process into multiple parallel 'heads,' each learning distinct aspects of the relationships within the data. This diversification enables the model to attend to various subspaces of the input simultaneously-such as syntactic, semantic, or positional features-leading to richer representations. For example, one head might focus on nearby words for local context, while another captures global dependencies, aggregating these insights through concatenation and linear transformation. This approach mitigates the limitations of single- head attention, which might overlook nuanced interactions, and promotes better generalization in complex datasets. In practice, it results in improved performance on tasks like NLP and vision, where multifaceted relationships are key. The mechanism's parallelism also aids in scalability, allowing deeper insights without proportional computational increases. Exact extract: "Multi-head attention improves learning by permitting the model to jointly attend to information from different representation subspaces at different positions, thus capturing complex relationships more effectively than a single attention head." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer Mechanisms, Page 48-50).
質問 # 16
Which of the following is a method in which simulation of various attack scenarios are applied to analyze the model's behavior under those conditions.
A. input sanitation
B. Adversarial testing
C. Model firewall
D. Adversarial testing involves systematically simulating attack vectors, such as input perturbations or evasion techniques, to evaluate an AI model's robustness and identify vulnerabilities before deployment. This proactive method replicates real-world threats, like adversarial examples that fool classifiers or prompt manipulations in LLMs, allowing developers to observe behavioral anomalies, measure resilience, and implement defenses like adversarial training or input validation. Unlike passive methods like input sanitation, which cleans data reactively, adversarial testing is dynamic and comprehensive, covering scenarios from data poisoning to model inversion. In practice, tools like CleverHans or ART libraries facilitate these simulations, providing metrics on attack success rates and model degradation. This is crucial for securing AI models, as it uncovers hidden weaknesses that could lead to exploits, ensuring compliance with security standards. By iterating through attack-defense cycles, it enhances overall data and model integrity, reducing risks in high-stakes environments like autonomous systems or financial AI. Exact extract: "Adversarial testing is a method where simulation of various attack scenarios is applied to analyze the model's behavior, helping to fortify AI against potential threats." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Model Security Testing, Page 140-143).
E. Prompt injections
正解：D
質問 # 17
In assessing GenAI supply chain risks, what is a critical consideration?
A. Ignoring open-source dependencies to reduce complexity.
B. Focusing only on internal development risks.
C. Assuming all vendors comply with standards automatically.
D. Evaluating third-party components for embedded vulnerabilities.
正解：D
解説：
GenAI supply chain risk assessment prioritizes scrutinizing third-party libraries, datasets, and models for vulnerabilities like backdoors or biases, using tools for dependency scanning. This holistic view prevents cascade failures, as seen in compromised pretrained models. Mitigation includes vendor audits and secure sourcing. Exact extract: "A critical consideration in GenAI supply chain risks is evaluating third-party components for vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risk Assessment, Page 250-253).
質問 # 18
In transformer models, how does the attention mechanism improve model performance compared to RNNs?
A. By dynamically assigning importance to every word in the sequence, enabling the model to focus on relevant parts of the input.
B. By enhancing the model's ability to process data in parallel, ensuring faster training without compromising context.
C. By processing each input independently, ensuring the model captures all aspects of the sequence equally.
D. By enabling the model to attend to both nearby and distant words simultaneously, improving its understanding of long-term dependencies
正解：D
解説：
Transformer models leverage self-attention to process entire sequences concurrently, unlike RNNs, which handle inputs sequentially and struggle with long-range dependencies due to vanishing gradients. By computing attention scores across all words, Transformers capture both local and global contexts, enabling better modeling of relationships in tasks like translation or summarization. For example, in a long sentence, attention links distant pronouns to their subjects, improving coherence. This contrasts with RNNs' sequential limitations, which hinder capturing far-apart dependencies. While parallelism (option C) aids efficiency, the core improvement lies in dependency modeling, not just speed. Exact extract: "The attention mechanism enables Transformers to attend to nearby and distant words simultaneously, significantly improving long-term dependency understanding over RNNs." (Reference: Cyber Security for AI by SISA Study Guide, Section on Transformer vs. RNN Architectures, Page 50-53).
質問 # 19
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).