2025 Latest CSPAI Exam Dumps Recently Updated 52 Questions [Q20-Q38]

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2025 Latest CSPAI Exam Dumps Recently Updated 52 Questions

SISA CSPAI Real 2025 Braindumps Mock Exam Dumps

QUESTION 20
In ISO 42001, what is required for AI risk treatment?

Identifying, analyzing, and evaluating AI-specific risks with treatment plans.

Ignoring risks below a certain threshold.

Delegating all risk management to external auditors.

Focusing only on post-deployment risks.

QUESTION 21
What is a primary step in the risk assessment model for GenAI data privacy?

Ignoring data sources to speed up assessment.

Conducting data flow mapping to identify privacy risks.

Limiting assessment to model outputs only.

Relying on vendor assurances without verification.

QUESTION 22
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?

Retraining the model with more comprehensive and accurate datasets.

Reducing the number of attention layers to speed up generation

Increasing the model’s output length to enhance response complexity.

Encouraging randomness in responses to explore more diverse outputs.

QUESTION 23
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain.
What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?

Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.

Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.

Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.

Single-task fine-tuning tends to degrade the model’s performance on the original tasks it was trained on.

QUESTION 24
What is a key concept behind developing a Generative AI (GenAI) Language Model (LLM)?

Operating only in supervised environments

Human intervention for every decision

Data-driven learning with large-scale datasets

Rule-based programming

QUESTION 25
How does GenAI contribute to incident response in cybersecurity?

By delaying responses to gather more data for analysis.

By automating playbook generation and response orchestration.

By manually reviewing each incident without AI assistance.

By focusing only on post-incident reporting.

QUESTION 26
In utilizing Giskard for vulnerability detection, what is a primary benefit of integrating this open-source tool into the security function?

Automatically patching vulnerabilities without additional configuration

Reducing the need for manual vulnerability assessment entirely

Enabling real-time detection of vulnerabilities with actionable insights.

Limiting its use to only high-priority vulnerabilities.

QUESTION 27
What is a common use of an LLM as a Secondary Chatbot?

To serve as a fallback or supplementary AI assistant for more complex queries

To replace the primary AI system

To handle tasks unrelated to the main application

To only manage user credentials

QUESTION 28
When integrating LLMs using a Prompting Technique, what is a significant challenge in achieving consistent performance across diverse applications?

Handling the security concerns that arise from dynamically generated prompts

Overcoming the lack of transparency in understanding how the LLM interprets varying prompt structures.

The need for optimizing prompt templates to ensure generalization across different contexts.

Reducing latency in generating responses to meet real-time application requirements.

QUESTION 29
In a time-series prediction task, how does an RNN effectively model sequential data?

By focusing on the overall sequence structure rather than individual time steps for a more holistic approach.

By processing each time step independently, optimizing the model’s performance over time.

By storing only the most recent time step, ensuring efficient memory usage for real-time predictions

By using hidden states to retain context from prior time steps, allowing it to capture dependencies across the sequence.

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?

Training a larger proprietary model to replace the open-source LLM

Shifting the assistant to a completely rule-based system to avoid reliance on user feedback.

Implementing reinforcement learning from human feedback (RLHF) to refine responses based on user input.

Reducing the amount of feedback integrated to speed up deployment.

QUESTION 31
In a financial technology company aiming to implement a specialized AI solution, which approach would most effectively leverage existing AI models to address specific industry needs while maintaining efficiency and accuracy?

Adopting a Foundation Model as the base and fine-tuning it with domain-specific financial data to enhance its capabilities for forecasting and risk assessment.

Integrating multiple separate Domain-Specific GenAI models for various financial functions without using a foundational model for consistency

Building a new, from scratch Domain-Specific GenAI model for financial tasks without leveraging preexisting models.

Using a general Large Language Model (LLM) without adaptation, relying solely on its broad capabilities to handle financial tasks.

QUESTION 32
How do ISO 42001 and ISO 27563 integrate for comprehensive AI governance?

By combining AI management with privacy standards to address both operational and data protection needs.

By replacing each other in different organizational contexts.

By focusing ISO 42001 on privacy and ISO 27563 on management.

By applying only to public sector AI systems.

QUESTION 33
How does the multi-head self-attention mechanism improve the model’s ability to learn complex relationships in data?

By forcing the model to focus on a single aspect of the input at a time.

By ensuring that the attention mechanism looks only at local context within the input

By simplifying the network by removing redundancy in attention layers.

By allowing the model to focus on different parts of the input through multiple attention heads

QUESTION 34
How does ISO 27563 support privacy in AI systems?

By providing guidelines for privacy-enhancing technologies in AI.

By mandating the use of specific encryption algorithms.

By limiting AI to non-personal data only.

By focusing on performance metrics over privacy.

QUESTION 35
When deploying LLMs in production, what is a common strategy for parameter-efficient fine-tuning?

Using external reinforcement learning to adjust the model’s parameters dynamically.

Freezing the majority of model parameters and only updating a small subset relevant to the task

Training the model from scratch on the target task to achieve optimal performance.

Implementing multiple independent models for each specific task instead of fine tuning a single model

QUESTION 36
In the Retrieval-Augmented Generation (RAG) framework, which of the following is the most critical factor for improving factual consistency in generated outputs?

Fine-tuning the generative model with synthetic datasets generated from the retrieved documents

Utilising an ensemble of multiple LLMs to cross-check the generated outputs.

Implementing a redundancy check by comparing the outputs from different retrieval modules.

Tuning the retrieval model to prioritize documents with the highest semantic similarity

QUESTION 37
Which of the following is a characteristic of domain-specific Generative AI models?

They are designed to run exclusively on quantum computers

They are tailored and fine-tuned for specific fields or industries

They are only used for computer vision tasks

They are trained on broad datasets covering multiple domains

QUESTION 38
What is the main objective of ISO 42001 in AI management systems?

To establish requirements for an AI management system within organizations.

To focus solely on technical specifications for AI algorithms.