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

NEW QUESTION # 41
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. Reducing the number of attention layers to speed up generation
B. Increasing the model's output length to enhance response complexity.
C. Retraining the model with more comprehensive and accurate datasets.
D. Encouraging randomness in responses to explore more diverse outputs.

Answer: C

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 # 42
How does machine learning improve the accuracy of predictive models in finance?

A. By relying exclusively on manual adjustments and human input for predictions.
B. By continuously learning from new data patterns to refine predictions
C. By avoiding any use of past data and focusing solely on current trends
D. By using historical data patterns to make predictions without updates

Answer: B

Explanation:
Machine learning enhances financial predictive models by continuously learning from new data, refining predictions for tasks like fraud detection or market forecasting. This adaptability leverages evolving patterns, unlike static historical or manual methods, and improves security posture through real-time anomaly detection. Exact extract: "ML improves financial predictive accuracy by continuously learning from new data patterns to refine predictions." (Reference: Cyber Security for AI by SISA Study Guide, Section on ML in Financial Security, Page 85-88).

NEW QUESTION # 43
What metric is often used in GenAI risk models to evaluate bias?

A. Fairness metrics like demographic parity or equalized odds.
B. Computational efficiency during training.
C. Number of parameters in the model.
D. Accuracy rate without considering demographics.

Answer: A

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 # 44
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 focusing only on the most recent word in the sentence to speed up translation
B. By assigning a constant weight to each word, ensuring uniform translation output
C. By processing words in strict sequential order, which is essential for capturing meaning
D. By considering all words in a sentence equally and simultaneously, allowing the model to establish long-range dependencies.

Answer: D

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 # 45
Which of the following is a primary goal of enforcing Responsible AI standards and regulations in the development and deployment of LLMs?

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

Answer: D

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 # 46
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Certified Security Professional in Artificial Intelligence Interactive Testing Engine & CSPAI Latest Training Guide & Certified Security Professional in Artificial Intelligence Self-Paced Training

CSPAI Online Exam, Reliable CSPAI Test Topics

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

Authoritative Valid Study CSPAI Questions | Amazing Pass Rate For CSPAI: Certified Security Professional in Artificial Intelligence | Accurate CSPAI Online Exam

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