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SISA CSPAI Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	Improving SDLC Efficiency Using Gen AI: This section of the exam measures skills of the AI Security Analyst and explores how generative AI can be used to streamline the software development life cycle. It emphasizes using AI for code generation, vulnerability identification, and faster remediation, all while ensuring secure development practices.
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.

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When you take SISA CSPAI practice exams again and again you get familiar with the Certified Security Professional in Artificial Intelligence (CSPAI) real test pressure and learn to handle it for better outcomes. Features of the web-based and desktop CSPAI Practice Exams are similar. The only difference is that the Certified Security Professional in Artificial Intelligence (CSPAI) web-based version works online.

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q21-Q26):

NEW QUESTION # 21
Which framework is commonly used to assess risks in Generative AI systems according to NIST?

A. A general IT risk assessment without AI-specific considerations.
B. The AI Risk Management Framework (AI RMF) for evaluating trustworthiness.
C. Focusing solely on financial risks associated with AI deployment.
D. Using outdated models from traditional software risk assessment.

Answer: B

Explanation:
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).

NEW QUESTION # 22
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 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.
B. It ensures that the model treats all words as equally important, regardless of their position in the sequence.
C. It simplifies the model's computations by merging all words into a single representation, regardless of their order
D. It speeds up processing by reducing the number of tokens the model needs to handle.

Answer: A

Explanation:
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).

NEW QUESTION # 23
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

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

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 # 24
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 underlying ML model and its training data.
C. The user interface of the AI application
D. The physical hardware running the AI system

Answer: B

Explanation:
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).

NEW QUESTION # 25
What is a potential risk of LLM plugin compromise?

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

Answer: A

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 # 26
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Pass Guaranteed SISA - Valid Exam CSPAI Duration

SISA CSPAI Exam Syllabus Topics:

Exam CSPAI Duration Makes Passing Certified Security Professional in Artificial Intelligence More Convenient

SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q21-Q26):

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