HOW EXAMTORRENT D-GAI-F-01 EXAM PRACTICE QUESTIONS CAN HELP YOU IN EXAM PREPARATION?

How ExamTorrent D-GAI-F-01 Exam Practice Questions Can Help You in Exam Preparation?

How ExamTorrent D-GAI-F-01 Exam Practice Questions Can Help You in Exam Preparation?

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Tags: Dump D-GAI-F-01 File, Exam D-GAI-F-01 Experience, D-GAI-F-01 Valid Exam Preparation, D-GAI-F-01 Exam Simulator Fee, Valid Exam D-GAI-F-01 Preparation

Studying from an updated practice material is necessary to get success in the EMC D-GAI-F-01 certification test on the first try. If you don't adopt this strategy, you will not be able to clear the Dell GenAI Foundations Achievement (D-GAI-F-01) examination. Failure in the Dell GenAI Foundations Achievement (D-GAI-F-01) test will lead to loss of confidence, time, and money. Don't worry because "ExamTorrent" is here to save you from these losses with its updated and real EMC D-GAI-F-01 exam questions.

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

TopicDetails
Topic 1
  • 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 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
  • 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 4
  • 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 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 (Q34-Q39):

NEW QUESTION # 34
What is the purpose of fine-tuning in the generative Al lifecycle?

  • A. To put text into a prompt to interact with the cloud-based Al system
  • B. To randomize all the statistical weights of the neural network
  • C. To customize the model for a specific task by feeding it task-specific content
  • D. To feed the model a large volume of data from a wide variety of subjects

Answer: C

Explanation:
Customization: Fine-tuning involves adjusting a pretrained model on a smaller dataset relevant to a specific task, enhancing its performance for that particular application.


NEW QUESTION # 35
A legal team is assessing the ethical issues related to Generative Al.
What is a significant ethical issue they should consider?

  • A. Improved customer service
  • B. Copyright and legal exposure
  • C. Enhanced creativity
  • D. Increased productivity

Answer: B

Explanation:
When assessing the ethical issues related to Generative AI, a legal team should consider copyright and legal exposure as a significant concern. Generative AI has the capability to produce new content that could potentially infringe on existing copyrights or intellectual property rights. This raises complex legal questions about the ownership of AI-generated content and the liability for any copyright infringement that may occur as a result of using Generative AI systems.
The Official Dell GenAI Foundations Achievement document likely addresses the ethical considerations of AI, including the potential for bias and the importance of developing a culture to reduce bias and increase trust in AI systems1. Additionally, it would cover the ethical issues principles and the impact of AI in business, which includes navigating the legal landscape and ensuring compliance with copyright laws1.
Improved customer service (Option OA), enhanced creativity (Option OB), and increased productivity (Option OC) are generally viewed as benefits of Generative AI rather than ethical issues. Therefore, the correct answer is D. Copyright and legal exposure, as it pertains to the ethical and legal challenges that must be navigated when implementing Generative AI technologies.


NEW QUESTION # 36
What is Artificial Narrow Intelligence (ANI)?

  • A. Al systems that can process beyond human capabilities
  • B. Al systems that can perform a specific task autonomously
  • C. Al systems that can perform any task autonomously
  • D. Al systems that can think and make decisions like humans

Answer: B

Explanation:
Artificial Narrow Intelligence (ANI) refers to AI systems that are designed to perform a specific task or a narrow set of tasks. The correct answer is option D. Here's a detailed explanation:
Definition of ANI:ANI, also known as weak AI, is specialized in one area. It can perform a particular function very well, such as facial recognition, language translation, or playing a game like chess.
Characteristics:Unlike general AI, ANI does not possess general cognitive abilities. It cannot perform tasks outside its specific domain without human intervention or retraining.
Examples:Siri, Alexa, and Google's search algorithms are examples of ANI. These systems excel in their designated tasks but cannot transfer their learning to unrelated areas.
References:
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1),
15-25.


NEW QUESTION # 37
A company is implementing governance in its Generative Al.
What is a key aspect of this governance?

  • A. Speed of deployment
  • B. Cost efficiency
  • C. Transparency
  • D. User interface design

Answer: C

Explanation:
Governance in Generative AI involves several key aspects, among which transparency is crucial.
Transparency in AI governance refers to the clarity and openness regarding how AI systems operate, the data they use, the decision-making processes they employ, and the way they are developed and deployed. It ensures that stakeholders understand AI processes and can trust the outcomes produced by AI systems.
The Official Dell GenAI Foundations Achievement document likely emphasizes the importance of transparency as part of ethical AI governance. It would discuss the need for clear communication about AI operations to build trust and ensure accountability1. Additionally, transparency is a foundational element in addressing ethical considerations, reducing bias, and ensuring that AI systems are used responsibly2.
User interface design (Option OB), speed of deployment (Option OC), and cost efficiency (Option OD) are important factors in the development and implementation of AI systems but are not specifically governance aspects. Governance focuses on the overarching principles and practices that guide the ethical and responsible use of AI, making transparency the key aspect in this context.


NEW QUESTION # 38
What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?

  • A. Supervised learning is common for fine tuning and customization, while unsupervised learning is common for base model training.
  • B. Supervised learning is common for base model training, while unsupervised learning is common for fine tuning and customization.
  • C. Supervised learning feeds a large corpus of raw data into the Al system, while unsupervised learning uses labeled data to teach the Al system what output is expected.
  • D. Supervised learning uses labeled data to teach the Al system what output is expected, while unsupervised learning feeds a large corpus of raw data into the Al system, which determines the appropriate weights in its neural network.

Answer: D

Explanation:
Supervised Learning: Involves using labeled datasets where the input-output pairs are provided. The AI system learns to map inputs to the correct outputs by minimizing the error between its predictions and the actual labels.


NEW QUESTION # 39
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