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EMC D-GAI-F-01 Exam Syllabus Topics:

Topic
Details

Topic 1

Use Cases and Applications: For business analysts and solution architects, this section might cover practical applications and use cases of Generative AI within Dell's ecosystem.

Topic 2

Dell's Generative AI Technologies: For Dell system administrators and AI implementers, this part of the exam probably focuses on Dell's specific implementations and tools related to Generative AI.

Topic 3

Ethics and Responsible AI: For all professionals working with AI, this section likely covers ethical considerations and responsible use of Generative AI in enterprise environments.

Topic 4

Introduction to Generative AI: For AI enthusiasts and IT professionals, this section of the exam likely covers the basic concepts and principles of Generative AI.

Topic 5

Implementation and Best Practices: For IT managers and system integrators, this part of the exam may address best practices for implementing Generative AI solutions using Dell technologies.

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EMC Dell GenAI Foundations Achievement Sample Questions (Q27-Q32):

NEW QUESTION # 27
A tech startup is developing a chatbot that can generate human-like text to interact with its users.
What is the primary function of the Large Language Models (LLMs) they might use?

A. To store data
B. To manage databases
C. To generate human-like text
D. To encrypt information

Answer: C

Explanation:
Large Language Models (LLMs), such as GPT-4, are designed to understand and generate human-like text.
They are trained on vast amounts of text data, which enables them to produce responses that can mimic human writing styles and conversation patterns. The primary function of LLMs in the context of a chatbot is to interact with users by generating text that is coherent, contextually relevant, and engaging.
The Dell GenAI Foundations Achievement document outlines the role of LLMs in generative AI, which includes their ability to generate text that resembles human language1. This is essential for chatbots, as they are intended to provide a conversational experience that is as natural and seamless as possible.
Storing data (Option OA), encrypting information (Option OB), and managing databases (Option OD) are not the primary functions of LLMs. While LLMs may be used in conjunction with systems that perform these tasks, their core capability lies in text generation, making Option OC the correct answer.

NEW QUESTION # 28
A team is working on mitigating biases in Generative Al.
What is a recommended approach to do this?

A. Focus on one language for training data
B. Use a single perspective during model development
C. Regular audits and diverse perspectives
D. Ignore systemic biases

Answer: C

Explanation:
Mitigating biases in Generative AI is a complex challenge that requires a multifaceted approach. One effective strategy is to conduct regular audits of the AI systems and the data they are trained on. These audits can help identify and address biases that may exist in the models. Additionally, incorporating diverse perspectives in the development process is crucial. This means involving a team with varied backgrounds and viewpoints to ensure that different aspects of bias are considered and addressed.
The Dell GenAI Foundations Achievement document emphasizes the importance of ethics in AI, including understanding different types of biases and their impacts, and fostering a culture that reduces bias to increase trust in AI systems12. It is likely that the document would recommend regular audits and the inclusion of diverse perspectives as part of a comprehensive strategy to mitigate biases in Generative AI.
Focusing on one language for training data (Option B), ignoring systemic biases (Option C), or using a single perspective during model development (Option D) would not be effective in mitigating biases and, in fact, could exacerbate them. Therefore, the correct answer is A. Regular audits and diverse perspectives.

NEW QUESTION # 29
What are the potential impacts of Al in business? (Select two)

A. Increasing the need for human intervention
B. Improving operational efficiency and enhancing customer experiences
C. Reducing production and operating costs
D. Limiting the use of data analytics

Answer: B,C

Explanation:
Reducing Costs: AI can automate repetitive and time-consuming tasks, leading to significant cost savings in production and operations. By optimizing resource allocation and minimizing errors, businesses can lower their operating expenses.

NEW QUESTION # 30
What is the significance ofparameters in Large Language Models (LLMs)?

A. Parameters are used to decrease the size of the LLMs.
B. Parameters are used to increase the size of the LLMs.
C. Parameters are used to parse image, audio, and video data in LLMs.
D. Parameters are statistical weights inside of the neural network of LLMs.

Answer: D

NEW QUESTION # 31
What is a principle thatguides organizations, government, and developers towards the ethical use of Al?

A. Only regulatory agencies should be held accountable for the accuracy, fairness, and use of Al models
B. Al models must ensure data privacy and confidentiality.
C. The value of Al models must only be measured in financial gain.
D. Al models must always agree with the user's point of view.

Answer: B

Explanation:
One of the guiding principles for the ethical use of AI is ensuring data privacy and confidentiality. Here's a detailed explanation:
Ethical Principle:
Explanation:Organizations, governments, and developers are increasingly recognizing the importance of protecting individuals' data. Ensuring data privacy and confidentiality is crucial to maintaining trust and compliance with legal standards.
Implementation:AI models must be designed to handle data responsibly, employing techniques such as encryption, anonymization, and secure data storage to protect sensitive information.
Regulatory Compliance:Adhering to regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is essential for legal and ethical AI deployment.
References:
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines.
Nature Machine Intelligence, 1(9), 389-399.
Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083),
20160360.

NEW QUESTION # 32
......

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