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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q29-Q34):

NEW QUESTION # 29
What role does GenAI play in automating vulnerability scanning and remediation processes?

A. By increasing the frequency of manual scans to ensure thoroughness.
B. By generating code patches and suggesting fixes based on vulnerability descriptions.
C. By ignoring low-priority vulnerabilities to focus on high-impact ones.
D. By compiling lists of vulnerabilities without any analysis.

Answer: B

Explanation:
GenAI automates vulnerability management by analyzing scan results and generating tailored code patches or remediation strategies, accelerating the fix process and reducing human error. Using natural language processing, it interprets vulnerability reports, cross-references with known exploits, and proposes secure code alternatives, integrating seamlessly into DevSecOps pipelines. This proactive approach minimizes exposure windows and enhances system resilience against exploits. For instance, in cloud environments, GenAI can simulate patch impacts before application. This contributes to a stronger security posture by enabling rapid, accurate responses to threats. Exact extract: "GenAI automates vulnerability scanning and remediation by generating code patches and fixes, improving efficiency and security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on Automation in Vulnerability Management, Page 205-208).

NEW QUESTION # 30
In a scenario where Open-Source LLMs are being used to create a virtual assistant, what would be the most effective way to ensure the assistant is continuously improving its interactions without constant retraining?

A. Reducing the amount of feedback integrated to speed up deployment.
B. Training a larger proprietary model to replace the open-source LLM
C. Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.
D. Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.

Answer: C

Explanation:
For continuous improvement in open-source LLM-based virtual assistants, RLHF integrates human evaluations to align model outputs with preferences, iteratively refining behavior without full retraining. This method uses reward models trained on feedback to guide policy optimization, enhancing interaction quality over time. It addresses limitations like initial biases or suboptimal responses by leveraging real-world user inputs, making the system adaptive and efficient. Unlike full retraining, RLHF is parameter-efficient and scalable, ideal for production environments. Security benefits include monitoring feedback for adversarial attempts. Exact extract: "Implementing RLHF allows continuous refinement of the assistant's interactions based on user feedback, avoiding the need for constant full retraining while improving performance." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Improvement Techniques in SDLC, Page 85-88).

NEW QUESTION # 31
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

A. Utilising an ensemble of multiple LLMs to cross-check the generated outputs.
B. Fine-tuning the generative model with synthetic datasets generated from the retrieved documents
C. Tuning the retrieval model to prioritize documents with the highest semantic similarity
D. Implementing a redundancy check by comparing the outputs from different retrieval modules.

Answer: C

Explanation:
The Retrieval-Augmented Generation (RAG) framework enhances generative models by incorporating external knowledge retrieval to ground outputs in factual data, thereby improving consistency and reducing hallucinations. The critical factor lies in optimizing the retrieval component to select documents with maximal semantic relevance, often using techniques like dense vector embeddings (e.g., via BERT or similar encoders) and similarity metrics such as cosine similarity. This ensures that the generator receives contextually precise information, minimizing irrelevant or misleading inputs that could lead to inconsistent outputs. For instance, in question-answering systems, prioritizing high-similarity documents allows the model to reference verified sources directly, boosting accuracy. Other approaches, like ensembles or redundancy checks, are supplementary but less foundational than effective retrieval tuning, which directly impacts the quality of augmented context. In SDLC, integrating RAG with fine-tuned retrieval accelerates development cycles by enabling modular updates without full model retraining. Security benefits include tracing outputs to sources for auditability, aligning with responsible AI practices. This method scales well for large knowledge bases, making it essential for production-grade applications where factual integrity is paramount. Exact extract:
"Tuning the retrieval model to prioritize documents with the highest semantic similarity is the most critical factor for improving factual consistency in RAG-generated outputs, as it ensures relevant context is provided to the generator." (Reference: Cyber Security for AI by SISA Study Guide, Section on RAG Frameworks in SDLC Efficiency, Page 95-98).

NEW QUESTION # 32
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. Encouraging randomness in responses to explore more diverse outputs.
B. Retraining the model with more comprehensive and accurate datasets.
C. Increasing the model's output length to enhance response complexity.
D. Reducing the number of attention layers to speed up generation

Answer: B

Explanation:
Hallucinations in AI, particularly LLMs, arise from gaps in training data, overfitting, or inadequate generalization, leading to plausible but false outputs. The most effective mitigation is retraining with expansive, high-quality datasets that cover diverse scenarios, ensuring factual grounding and reducing fabrication risks. This involves curating verified sources, incorporating fact-checking mechanisms, and using techniques like data augmentation to fill knowledge voids. Complementary strategies include prompt engineering and external verification, but foundational retraining addresses root causes, enhancing overall trustworthiness. In security contexts, this prevents misinformation propagation, critical for applications in decision-making or content generation. Exact extract: "To reduce hallucinations and improve trustworthiness, retrain the model with more comprehensive and accurate datasets, ensuring better factual alignment and reduced erroneous confidence in outputs." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Risks and Mitigations, Page 120-123).

NEW QUESTION # 33
An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?

A. Increased model efficiency in processing and generation tasks.
B. Improved model performance due to higher data volume.
C. Compromised model integrity and reliability leading to inaccurate or biased outputs
D. Enhanced model adaptability to diverse data types.

Answer: C

Explanation:
Poisoned datasets introduce adversarial perturbations or malicious samples that, when used in training, can subtly alter a model's decision boundaries, leading to degraded integrity and unreliable outputs. This risk manifests as backdoors or biases, where the model performs well on clean data but fails or behaves maliciously on triggered inputs, compromising security in applications like classification or generation. For instance, in a facial recognition system, poisoned data might cause misidentification of certain groups, resulting in biased or inaccurate results. Mitigation involves rigorous data validation, anomaly detection, and diverse sourcing to ensure dataset purity. The consequence extends to ethical concerns, potential legal liabilities, and loss of trust in AI systems. Addressing this requires ongoing monitoring and adversarial training to bolster resilience. Exact extract: "Using poisoned datasets can compromise model integrity, leading to inaccurate, biased, or manipulated outputs, which undermines the reliability of AI systems and poses significant security risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Poisoning Risks, Page 112-115).

NEW QUESTION # 34
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CSPAI Exam Objectives | CSPAI Dump

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q29-Q34):

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