D-GAI-F-01 Originale Fragen & D-GAI-F-01 Exam

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Neueste Dell GenAI Foundations Achievement Prüfung pdf & D-GAI-F-01 Prüfung Torrent

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EMC D-GAI-F-01 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 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.
Thema 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.
Thema 5	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.

EMC Dell GenAI Foundations Achievement D-GAI-F-01 Prüfungsfragen mit Lösungen (Q47-Q52):

47. Frage
Why is diversity important in Al training data?

A. To make Al models cheaper to develop
B. To reduce the storage requirements for data
C. To ensure the model can generalize across different scenarios
D. To increase the model's speed of computation

Antwort: C

Begründung:
Diversity in AI training data is crucial for developing robust and fair AI models. The correct answer is option C: Here's why:
Generalization:A diverse training dataset ensures that the AI model can generalize well across different scenarios and perform accurately in real-world applications.
Bias Reduction:Diverse data helps in mitigating biases that can arise from over-representation or under-representation of certain groups or scenarios.
Fairness and Inclusivity:Ensuring diversity in data helps in creating AI systems that are fair and inclusive, which is essential for ethical AI development.
References:
Barocas, S., Hardt, M., & Narayanan, A. (2019).Fairness and Machine Learning. fairmlbook.org.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54(6), 1-35.

48. Frage
A tech company is developing ethical guidelines for its Generative Al.
What should be emphasized in these guidelines?

A. Cost reduction
B. Speed of implementation
C. Fairness, transparency, and accountability
D. Profit maximization

Antwort: C

Begründung:
When developing ethical guidelines for Generative AI, it is essential to emphasize fairness, transparency, and accountability. These principles are fundamental to ensuring that AI systems are used responsibly and ethically.
* Fairness ensures that AI systems do not create or reinforce unfair bias or discrimination.
* Transparency involves clear communication about how AI systems work, the data they use, and the decision-making processes they employ.
* Accountability means that there are mechanisms in place to hold the creators and operators of AI systems responsible for their performance and impact.
The Official Dell GenAI Foundations Achievement document underscores the importance of ethics in AI, including the need to address various ethical issues, types of biases, and the culture that should be developed to reduce bias and increase trust in AI systems1. It also highlights the concepts of building an AI ecosystem and the impact of AI in business, which includes ethical considerations1.
Cost reduction (Option OA), speed of implementation (Option B), and profit maximization (Option OC) are important business considerations but do not directly relate to the ethical use of AI. Ethical guidelines are specifically designed to ensure that AI is used in a way that is just, open, and responsible, making Option OD the correct emphasis for these guidelines.

49. Frage
What are the three broad steps in the lifecycle of Al for Large Language Models?

A. Initialization, Training, and Deployment
B. Training, Customization, and Inferencing
C. Preprocessing, Training, and Postprocessing
D. Data Collection, Model Building, and Evaluation

Antwort: B

Begründung:
Training: The initial phase where the model learns from a large dataset. This involves feeding the model vast amounts of text data and using techniques like supervised or unsupervised learning to adjust the model's parameters.

50. Frage
A team is working on improving an LLM and wants to adjust the prompts to shape the model's output.
What is this process called?

A. Transfer Learning
B. Self-supervised Learning
C. Adversarial Training
D. P-Tuning

Antwort: D

Begründung:
The process of adjusting prompts to influence the output of a Large Language Model (LLM) is known as P-Tuning. This technique involves fine-tuning the model on a set of prompts that are designed to guide the model towards generating specific types of responses. P-Tuning stands for Prompt Tuning, where "P" represents the prompts that are used as a form of soft guidance to steer the model's generation process.
In the context of LLMs, P-Tuning allows developers to customize the model's behavior without extensive retraining on large datasets. It is a more efficient method compared to full model retraining, especially when the goal is to adapt the model to specific tasks or domains.
The Dell GenAI Foundations Achievement document would likely cover the concept of P-Tuning as it relates to the customization and improvement of AI models, particularly in the field of generative AI12. This document would emphasize the importance of such techniques in tailoring AI systems to meet specific user needs and improving interaction quality.
Adversarial Training (Option OA) is a method used to increase the robustness of AI models against adversarial attacks. Self-supervised Learning (Option OB) refers to a training methodology where the model learns from data that is not explicitly labeled. Transfer Learning (Option OD) is the process of applying knowledge from one domain to a different but related domain. While these are all valid techniques in the field of AI, they do not specifically describe the process of using prompts to shape an LLM's output, making Option OC the correct answer.

51. Frage
In a Generative Adversarial Network (GAN), you have a network that evaluates whether the data generated by the other network is real or fake. What is this evaluating network called?

A. Discriminator
B. Generator
C. Encoder
D. Decoder

Antwort: A

52. Frage
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D-GAI-F-01 Exam: https://www.it-pruefung.com/D-GAI-F-01.html

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D-GAI-F-01 Originale Fragen & D-GAI-F-01 Exam

Neueste Dell GenAI Foundations Achievement Prüfung pdf & D-GAI-F-01 Prüfung Torrent

EMC D-GAI-F-01 Prüfungsplan:

EMC Dell GenAI Foundations Achievement D-GAI-F-01 Prüfungsfragen mit Lösungen (Q47-Q52):

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