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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q43-Q48):

NEW QUESTION # 43
What is a potential risk associated with hallucinations in LLMs, and how should it be addressed to ensure Responsible AI?

A. Hallucinations can produce inaccurate or misleading information; it should be addressed by incorporating external knowledge bases and retrieval systems.
B. Hallucinations cause models to slow down; optimizing hardware performance is necessary to mitigate this issue.
C. Hallucinations can lead to creative outputs, which are beneficial for all applications; hence, no measures are necessary.
D. Hallucinations are primarily due to overfitting; regularization techniques should be applied during training.

Answer: A

Explanation:
Hallucinations in LLMs risk generating inaccurate or misleading outputs, undermining trust and safety.
Incorporating external knowledge bases and retrieval systems, like RAG, grounds responses in verified data, reducing fabrications and aligning with Responsible AI principles. Regularization helps but is secondary to factual grounding. Exact extract: "Hallucinations produce misleading information, addressed by incorporating external knowledge bases and retrieval systems for Responsible AI." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Hallucination Mitigation, Page 125-128).

NEW QUESTION # 44
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 underlying ML model and its training data.
B. The marketing materials associated with the AI product
C. The physical hardware running the AI system
D. The user interface of the AI application

Answer: A

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 # 45
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

A. Using external reinforcement learning to adjust the model's parameters dynamically.
B. Freezing the majority of model parameters and only updating a small subset relevant to the task
C. Implementing multiple independent models for each specific task instead of fine tuning a single model
D. Training the model from scratch on the target task to achieve optimal performance.

Answer: B

Explanation:
Parameter-efficient fine-tuning (PEFT) strategies, like LoRA or adapters, freeze most pretrained parameters and train only lightweight modules, reducing computational costs while adapting to new tasks. This preserves general knowledge, prevents catastrophic forgetting, and enables quick deployments in resource-constrained settings. For LLMs, it's crucial for efficiency in production, allowing specialization without retraining billions of parameters. Security-wise, it minimizes exposure to new data risks. Exact extract: "A common strategy is freezing the majority of model parameters and updating only a small task-relevant subset, ensuring efficiency in fine-tuning for production deployment." (Reference: Cyber Security for AI by SISA Study Guide, Section on Efficient Fine-Tuning in SDLC, Page 90-92).

NEW QUESTION # 46
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 assigning a constant weight to each word, ensuring uniform translation output
B. By considering all words in a sentence equally and simultaneously, allowing the model to establish long-range dependencies.
C. By focusing only on the most recent word in the sentence to speed up translation
D. By processing words in strict sequential order, which is essential for capturing meaning

Answer: B

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 # 47
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. Enhanced the precision and relevance of automated outputs with reduced manual tuning.
B. Increased system complexity and the requirement for specialized knowledge,
C. Improved scalability and performance in handling diverse and evolving data.
D. Enabled more dynamic decision-making and adaptability with minimal manual intervention

Answer: D

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 # 48
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Valid CSPAI Test Materials & CSPAI Torrent

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q43-Q48):

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