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CSPAI Test Questions Fee, Key CSPAI Concepts

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q41-Q46):

NEW QUESTION # 41
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.

Answer: B

Explanation:
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).

NEW QUESTION # 42
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?

A. Limiting its use to only high-priority vulnerabilities.
B. Automatically patching vulnerabilities without additional configuration
C. Reducing the need for manual vulnerability assessment entirely
D. Enabling real-time detection of vulnerabilities with actionable insights.

Answer: D

Explanation:
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).

NEW QUESTION # 43
In what way can GenAI assist in phishing detection and prevention?

A. By sending automated phishing emails to test employee awareness.
B. By blocking all incoming emails to prevent any potential threats.
C. By relying solely on signature-based detection methods.
D. By generating realistic phishing simulations and analyzing user responses.

Answer: D

Explanation:
GenAI bolsters phishing defenses by creating sophisticated simulation campaigns that mimic real attacks, training employees and refining detection algorithms based on interaction data. It analyzes email content, URLs, and attachments semantically to identify subtle manipulations, going beyond traditional filters. This dynamic method adapts to evolving tactics like AI-generated deepfakes in emails, improving prevention through predictive modeling. Organizations benefit from reduced successful breach rates and enhanced user education. Integration with email gateways provides real-time alerts, strengthening overall security. Exact extract: "GenAI assists in phishing detection by generating simulations and analyzing responses, thereby preventing attacks and improving security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI in Phishing Mitigation, Page 210-213).

NEW QUESTION # 44
What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?

A. Data-driven learning with large-scale datasets
B. Rule-based programming
C. Operating only in supervised environments
D. Human intervention for every decision

Answer: A

Explanation:
GenAI LLMs rely on data-driven learning, leveraging vast datasets to model language patterns, semantics, and contexts through unsupervised or semi-supervised methods. This enables scalability and adaptability, unlike rule-based systems or human-dependent approaches. Large datasets drive generalization, though they introduce security challenges like data quality control. Exact extract: "A key concept of GenAI LLMs is data- driven learning with large-scale datasets, enabling robust language modeling." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI Development Principles, Page 60-63).

NEW QUESTION # 45
In the context of LLM plugin compromise, as demonstrated by the ChatGPT Plugin Privacy Leak case study, what is a key practice to secure API access and prevent unauthorized information leaks?

A. Increasing the frequency of API endpoint updates.
B. Implementing stringent authentication and authorization mechanisms, along with regular security audits
C. Allowing open API access to facilitate ease of integration
D. Restricting API access to a predefined list of IP addresses

Answer: B

Explanation:
The ChatGPT Plugin Privacy Leak highlighted vulnerabilities in plugin ecosystems, where weak API security led to data exposure. Implementing robust authentication (e.g., OAuth) and authorization (e.g., RBAC), coupled with regular audits, ensures only verified entities access APIs, preventing leaks. IP whitelisting is less comprehensive, and open access heightens risks. Audits detect misconfigurations, aligning with secure AI practices. Exact extract: "Stringent authentication, authorization, and regular audits are key to securing API access and preventing leaks in LLM plugins." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security Case Studies, Page 170-173).

NEW QUESTION # 46
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Glen West Glen West

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