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Free PDF Quiz 2025 CSPAI: Certified Security Professional in Artificial Intelligence Latest Valid Test Pdf

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q35-Q40):

NEW QUESTION # 35
What does the OCTAVE model emphasize in GenAI risk assessment?

A. Short-term tactical responses over strategic planning.
B. Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.
C. Exclusion of stakeholder input in assessments.
D. Solely technical vulnerabilities in AI models.

Answer: B

Explanation:
OCTAVE adapts to GenAI by emphasizing organizational risk perspectives, identifying critical assets like models and data, evaluating threats, and prioritizing mitigations through stakeholder collaboration. It fosters a strategic, enterprise-wide approach to AI risks, integrating business impacts. Exact extract: "OCTAVE emphasizes operational critical threat, asset, and vulnerability evaluation in GenAI risk assessment." (Reference: Cyber Security for AI by SISA Study Guide, Section on OCTAVE for AI, Page 255-258).

NEW QUESTION # 36
A company developing AI-driven medical diagnostic tools is expanding into the European market. To ensure compliance with local regulations, what should be the company's primary focus in adhering to the EU AI Act?

A. Implementing measures to prevent any harmful outcomes and ensure AI system safety
B. Focusing on integrating ethical guidelines to ensure AI decisions are fair and unbiased.
C. Prioritizing transparency and accountability in AI systems to avoid high-risk categorization
D. Ensuring the AI system meets stringent privacy standards to protect sensitive data

Answer: A

Explanation:
The EU AI Act classifies AI systems by risk, with medical diagnostics as high-risk, requiring stringent safety measures to prevent harm, such as misdiagnoses. Compliance prioritizes robust testing, validation, and monitoring to ensure safe outcomes, aligning with ISO 42001's risk management framework. While ethics and privacy are critical, safety is the primary focus to meet regulatory thresholds and protect users. Exact extract: "The EU AI Act emphasizes implementing measures to prevent harmful outcomes and ensure AI system safety, particularly for high-risk applications like medical diagnostics." (Reference: Cyber Security for AI by SISA Study Guide, Section on EU AI Act Compliance, Page 175-178).

NEW QUESTION # 37
How does machine learning improve the accuracy of predictive models in finance?

A. By continuously learning from new data patterns to refine predictions
B. By using historical data patterns to make predictions without updates
C. By avoiding any use of past data and focusing solely on current trends
D. By relying exclusively on manual adjustments and human input for predictions.

Answer: A

Explanation:
Machine learning enhances financial predictive models by continuously learning from new data, refining predictions for tasks like fraud detection or market forecasting. This adaptability leverages evolving patterns, unlike static historical or manual methods, and improves security posture through real-time anomaly detection. Exact extract: "ML improves financial predictive accuracy by continuously learning from new data patterns to refine predictions." (Reference: Cyber Security for AI by SISA Study Guide, Section on ML in Financial Security, Page 85-88).

NEW QUESTION # 38
In a machine translation system where context from both early and later words in a sentence is crucial, a team is considering moving from RNN-based models to Transformer models. How does the self-attention mechanism in Transformer architecture support this task?

A. By assigning a constant weight to each word, ensuring uniform translation output
B. By considering all words in a sentence equally and simultaneously, allowing the model to establish long-range dependencies.
C. By processing words in strict sequential order, which is essential for capturing meaning
D. By focusing only on the most recent word in the sentence to speed up translation

Answer: B

Explanation:
The self-attention mechanism in Transformer models revolutionizes machine translation by enabling the model to weigh the importance of different words in a sentence relative to each other, regardless of their position. Unlike RNN-based models, which process sequences sequentially and often struggle with long-range dependencies due to vanishing gradients, Transformers use self-attention to compute representations of all words in parallel. This allows the model to capture contextual relationships between distant words effectively, such as linking pronouns to their antecedents across long sentences. For instance, in translating a sentence where the meaning depends on both the beginning and end, self-attention assigns dynamic weights based on query, key, and value matrices, facilitating a global view of the input. This parallelism not only improves accuracy in tasks requiring comprehensive context but also enhances training efficiency. The mechanism supports bidirectional context understanding, making it superior for natural language processing tasks like translation. Exact extract: "The self-attention mechanism allows the model to consider all positions in the input sequence simultaneously, establishing long-range dependencies that are critical for context-heavytasks like machine translation, unlike sequential RNN processing." (Reference: Cyber Security for AI by SISA Study Guide, Section on Evolution of AI Architectures, Page 45-47).

NEW QUESTION # 39
What is a potential risk of LLM plugin compromise?

A. Reduced model training time
B. Better integration with third-party tools
C. Improved model accuracy
D. Unauthorized access to sensitive information through compromised plugins

Answer: D

Explanation:
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).

NEW QUESTION # 40
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