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SISA CSPAI Sample Questions
Question # 1
What is a common use of an LLM as a Secondary Chatbot?
A. To serve as a fallback or supplementary AI assistant for more complex queries B. To replace the primary AI system C. To handle tasks unrelated to the main application D. To only manage user credentials
Answer: A EXPLANATION:
A Secondary Chatbot is often deployed as an expert "layer" that takes over when a primary, simpler bot (like a rule-based
system) reaches its functional limits. It provides nuanced, context-aware answers to complex or technical user questions,
ensuring a seamless customer experience without the need for immediate human intervention.
Question # 2
In what way can GenAI assist in phishing detection and prevention?
A. By sending automated phishing emails to test employee awareness. B. By generating realistic phishing simulations and analyzing user responses. C. By blocking all incoming emails to prevent any potential threats. D. By relying solely on signature-based detection methods.
Answer: B
Explanation:
GenAI enhances phishing prevention by creating highly sophisticated, dynamic simulations that mimic real-world attacks, allowing
organizations to train employees against modern threats like spear-phishing. It also assists in detection by using Natural Language
Processing (NLP) to analyze the intent and tone of incoming emails, flagging subtle anomalies that traditional signature-based filters
might miss.
Question # 3
An AI system is generating confident but incorrect outputs, commonly known as hallucinations.
Which strategy would most likely reduce the occurrence of such hallucinations and improve the
trustworthiness of the system?
A. Retraining the model with more comprehensive and accurate datasets. B. Reducing the number of attention layers to speed up generation C. Increasing the model's output length to enhance response complexity. D. Encouraging randomness in responses to explore more diverse outputs.
Answer: A
Explanation:
Hallucinations in GenAI occur when a model generates factually incorrect information with high statistical confidence, often due to
gaps or noise in its underlying training data. Strategy A addresses the root cause by providing the model with higher-quality, verified
information, which helps it establish a more accurate "world model." Additionally, techniques like Retrieval Augmented Generation
(RAG) which anchors the model to external, trusted facts or fine tuning on specialized datasets are common industry standards for
grounding outputs. In contrast, increasing randomness (Option D) or lengthening responses (Option C) would likely increase the
chance of the AI "wandering" into fabricated territory, while reducing attention layers (Option B) would simply degrade the model's
ability to understand context.
Question # 4
How does ISO 27563 support privacy in AI systems?
A. By providing guidelines for privacy-enhancing technologies in AI. B. By mandating the use of specific encryption algorithms. C. By limiting AI to non-personal data only. D. By focusing on performance metrics over privacy.
Answer: A
Explanation:
ISO/IEC 27563 is a critical technical report that addresses the intersection of artificial intelligence and data protection by focusing on
Privacy-Enhancing Technologies (PETs). It provides a framework for organizations to implement methods like differential
privacy, homomorphic encryption, and federated learning, which allow AI models to be trained or queried without exposing the
underlying sensitive personal data. Rather than simply banning the use of personal information, the standard offers a roadmap for
"Privacy by Design," ensuring that as AI systems become more complex, the methods used to de-identify and protect user data evolve
to meet those new technical challenges.
Question # 5
How does GenAI contribute to incident response in cybersecurity?
A. By delaying responses to gather more data for analysis. B. By automating playbook generation and response orchestration. C. By manually reviewing each incident without AI assistance. D. By focusing only on post-incident reporting.
Answer: B
Explanation:
In modern cybersecurity, GenAI acts as a "force multiplier" for Incident Response (IR) teams by handling the heavy lifting of
documentation and coordination. Instead of analysts manually writing response steps during a crisis, GenAI can instantly generate
tailored playbooks based on the specific type of attack detected. It also assists in orchestration automatically connecting different
security tools to isolate infected hosts, block malicious IP addresses, or disable compromised accounts in seconds. By summarizing
complex log data into plain language and drafting executive reports, GenAI allows human responders to focus on high-level strategy
rather than administrative tasks, significantly reducing the Mean Time to Respond (MTTR).
Question # 6
A company's chatbot, Tay, was poisoned by malicious interactions. What is the primary lesson
learned from this case study?
A. Continuous live training is essential for enhancing chatbot performance. B. Encrypting user data can prevent such attacks C. Open interaction with users without safeguards can lead to model poisoning and generation of
inappropriate content. D. Chatbots should have limited conversational abilities to prevent poisoning.
Answer: C
Explanation: The case of Microsoft’s "Tay" is a landmark lesson in Data Poisoning and the risks of uncontrolled online learning. Because Tay was
designed to learn and mimic language patterns from real-time interactions on social media without robust moderation filters or "sanity
checks," malicious users were able to coordinate and feed the bot offensive data. This quickly corrupted the model’s behavior, causing
it to generate highly inappropriate and harmful content. The primary takeaway for AI developers is that GenAI systems—especially
those that adapt based on user input—require strict input validation, output filtering, and curated training sets to maintain safety
and alignment.
Question # 7
How does the STRIDE model adapt to assessing threats in GenAI?
A. By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and
Elevation of Privilege to AI components. B. By focusing only on hardware threats in AI systems. C. By excluding AI-specific threats like model inversion. D. By using it unchanged from traditional software.
ANSWER:A Explanation:
Adapting the STRIDE model to Generative AI involves mapping its six traditional threat categories Spoofing, Tampering,
Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to the unique architecture of AI systems. Instead
of just looking at standard code vulnerabilities, security professionals use STRIDE to identify AI-specific risks such as prompt
injection (Tampering), model inversion (Information Disclosure) where sensitive training data is leaked, and resource exhaustion
(Denial of Service) via complex queries that overwhelm GPU capacity. By applying this structured framework to the data pipeline, the
model weights, and the inference API, organizations can systematically secure the entire GenAI lifecycle.
Question # 8
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?
A. By combining AI management with privacy standards to address both operational and data
protection needs. B. By replacing each other in different organizational contexts. C. By focusing ISO 42001 on privacy and ISO 27563 on management. D. By applying only to public sector AI systems.
Answer: A
Explanation:
These two standards are designed to be complementary, forming a "double-layer" of protection that covers both the organizational
process and the technical data privacy requirements.
Question # 9
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 ensuring that the attention mechanism looks only at local context within the input C. By simplifying the network by removing redundancy in attention layers. D. By allowing the model to focus on different parts of the input through multiple attention heads
Answer: D
Explanation:
The Multi-Head Self-Attention mechanism is a core innovation of the Transformer architecture. Instead of having a single "eye" look
at the data, it uses multiple "heads" (independent attention mechanisms) to process the input simultaneously. Each head can focus on a
different type of relationship—for example, one head might focus on grammar, another on subject-verb agreement, and another on
historical context.
Question # 10
What is the main objective of ISO 42001 in AI management systems?
A. To establish requirements for an AI management system within organizations. B. To focus solely on technical specifications for AI algorithms. C. To regulate hardware used in AI deployments. D. To provide guidelines only for small-scale AI projects.
Answer: A
Explanation:
ISO/IEC 42001 is the world’s first international standard for an Artificial Intelligence Management System (AIMS). Unlike
standards that focus purely on technical code or specific hardware, ISO 42001 provides a high-level framework for how an
organization should govern its AI throughout its entire lifecycle.
