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Hot Certification CSPAI Exam Cost 100% Pass | Pass-Sure Free CSPAI Practice Exams: Certified Security Professional in Artificial Intelligence

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SISA CSPAI Exam Syllabus Topics:

Topic
Details

Topic 1

Evolution of Gen AI and Its Impact: This section of the exam measures skills of the AI Security Analyst and covers how generative AI has evolved over time and the implications of this evolution for cybersecurity. It focuses on understanding the broader impact of Gen AI technologies on security operations, threat landscapes, and risk management strategies.

Topic 2

Models for Assessing Gen AI Risk: This section of the exam measures skills of the Cybersecurity Risk Manager and deals with frameworks and models used to evaluate risks associated with deploying generative AI. It includes methods for identifying, quantifying, and mitigating risks from both technical and governance perspectives.

Topic 3

Using Gen AI for Improving the Security Posture: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on how Gen AI tools can strengthen an organization’s overall security posture. It includes insights on how automation, predictive analysis, and intelligent threat detection can be used to enhance cyber resilience and operational defense.

Topic 4

Securing AI Models and Data: This section of the exam measures skills of the Cybersecurity Risk Manager and focuses on the protection of AI models and the data they consume or generate. Topics include adversarial attacks, data poisoning, model theft, and encryption techniques that help secure the AI lifecycle.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q32-Q37):

NEW QUESTION # 32
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. Training the model from scratch on the target task to achieve optimal performance.
C. Implementing multiple independent models for each specific task instead of fine tuning a single model
D. Freezing the majority of model parameters and only updating a small subset relevant to the task

Answer: D

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 # 33
Which framework is commonly used to assess risks in Generative AI systems according to NIST?

A. A general IT risk assessment without AI-specific considerations.
B. Focusing solely on financial risks associated with AI deployment.
C. The AI Risk Management Framework (AI RMF) for evaluating trustworthiness.
D. Using outdated models from traditional software risk assessment.

Answer: C

Explanation:
The NIST AI Risk Management Framework (AI RMF) provides a structured approach to identify, assess, and mitigate risks in GenAI, emphasizing trustworthiness attributes like safety, fairness, and explainability. It categorizes risks into governance, mapping, measurement, and management phases, tailored for AI lifecycles.
For GenAI, it addresses unique risks such as hallucinations or bias amplification. Organizations apply it to conduct impact assessments and implement controls, ensuring compliance and ethical deployment. Exact extract: "NIST's AI RMF is commonly used to assess risks in Generative AI, focusing on trustworthiness and lifecycle management." (Reference: Cyber Security for AI by SISA Study Guide, Section on NIST Frameworks for AI Risk, Page 230-233).

NEW QUESTION # 34
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 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 # 35
An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?

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

Answer: A

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 # 36
What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?

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

Answer: D

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 # 37
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