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100% Pass 2025 First-grade SISA CSPAI: Test Certified Security Professional in Artificial Intelligence Question

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SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	Using Gen AI for Improving the Security Posture: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on how Gen AI tools can strengthen an organization’s overall security posture. It includes insights on how automation, predictive analysis, and intelligent threat detection can be used to enhance cyber resilience and operational defense.
Topic 2	AIMS and Privacy Standards: ISO 42001 and ISO 27563: This section of the exam measures skills of the AI Security Analyst and addresses international standards related to AI management systems and privacy. It reviews compliance expectations, data governance frameworks, and how these standards help align AI implementation with global privacy and security regulations.
Topic 3	Securing AI Models and Data: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on the protection of AI models and the data they consume or generate. Topics include adversarial attacks, data poisoning, model theft, and encryption techniques that help secure the AI lifecycle.
Topic 4	Evolution of Gen AI and Its Impact: This section of the exam measures skills of the AI Security Analyst and covers how generative AI has evolved over time and the implications of this evolution for cybersecurity. It focuses on understanding the broader impact of Gen AI technologies on security operations, threat landscapes, and risk management strategies.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q42-Q47):

NEW QUESTION # 42
During the development of AI technologies, how did the shift from rule-based systems to machine learning models impact the efficiency of automated tasks?

A. Enabled more dynamic decision-making and adaptability with minimal manual intervention
B. Improved scalability and performance in handling diverse and evolving data.
C. Enhanced the precision and relevance of automated outputs with reduced manual tuning.
D. Increased system complexity and the requirement for specialized knowledge,

Answer: A

Explanation:
The transition from rigid rule-based systems, which rely on predefined logic and struggle with variability, to machine learning models introduced data-driven learning, allowing systems to adapt dynamically to new patterns with less human oversight. This shift boosted efficiency in automated tasks by enabling real-time adjustments, such as in spam detection where ML models evolve with threats, unlike static rules. It minimized manual rule updates, fostering scalability and handling complex, unstructured data effectively. However, it introduced challenges like interpretability needs. In GenAI evolution, this paved the way for advanced models like Transformers, impacting sectors by automating nuanced decisions. Exact extract: "The shift enabled more dynamic decision-making and adaptability with minimal manual intervention, significantly improving the efficiency of automated tasks." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Evolution and Impacts, Page 20-23).

NEW QUESTION # 43
How does the STRIDE model adapt to assessing threats in GenAI?

A. By excluding AI-specific threats like model inversion.
B. By focusing only on hardware threats in AI systems.
C. By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to AI components.
D. By using it unchanged from traditional software.

Answer: C

Explanation:
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).

NEW QUESTION # 44
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

A. Focusing solely on improving the speed and scalability of AI systems
B. Maximizing model performance while minimizing computational costs.
C. Ensuring that AI systems operate safely, ethically, and without causing harm.
D. Developing AI systems with the highest accuracy regardless of data privacy concerns

Answer: C

Explanation:
Responsible AI standards, including ISO 42001 for AI management systems, aim to promote ethical development, ensuring safety, fairness, and harm prevention in LLM deployments. This encompasses bias mitigation, transparency, and accountability, aligning with societal values. Regulations like the EU AI Act reinforce this by categorizing risks and mandating safeguards. The goal transcends performance to foster trust and sustainability, addressing issues like discrimination or misuse. Exact extract: "The primary goal is to ensure AI systems operate safely, ethically, and without causing harm, as outlined in standards like ISO
42001." (Reference: Cyber Security for AI by SISA Study Guide, Section on Responsible AI and ISO Standards, Page 150-153).

NEW QUESTION # 45
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 considering all words in a sentence equally and simultaneously, allowing the model to establish long-range dependencies.
B. By assigning a constant weight to each word, ensuring uniform translation output
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: A

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 # 46
Which of the following is a potential use case of Generative AI specifically tailored for CXOs (Chief Experience Officers)?

A. Conducting genetic sequencing for personalized medicine
B. Developing autonomous vehicles for urban mobility solutions.
C. Enhancing customer support through AI-powered chatbots that provide 24/7 assistance.
D. Automating financial transactions in blockchain networks.

Answer: C

Explanation:
For CXOs focused on customer experience, Generative AI excels in powering chatbots that deliver round-the- clock, personalized support, addressing queries with context-aware responses. This enhances user satisfaction by reducing wait times and tailoring interactions using predictive analytics, while integrated security measures like anomaly detection safeguard against threats like phishing. Unlike unrelated applications like autonomous vehicles or genetic sequencing, chatbots directly align with CXO goals of improving engagement and trust.
Security posture is bolstered by monitoring interactions for malicious inputs, ensuring safe AI-driven CX.
Exact extract: "Generative AI enhances customer support through AI-powered chatbots providing 24/7 assistance, tailored for CXOs to improve engagement and security." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI for CX Enhancement, Page 75-78).

NEW QUESTION # 47
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CSPAI PDF study guide & SISA CSPAI test-king

100% Pass 2025 First-grade SISA CSPAI: Test Certified Security Professional in Artificial Intelligence Question

SISA CSPAI Exam Syllabus Topics:

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q42-Q47):

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