POPULAR D-GAI-F-01 STUDY MATERIALS GIVE YOU EXCELLENT EXAM BRAINDUMPS - VERIFIEDDUMPS

Popular D-GAI-F-01 Study Materials Give You Excellent Exam Braindumps - VerifiedDumps

Popular D-GAI-F-01 Study Materials Give You Excellent Exam Braindumps - VerifiedDumps

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EMC Dell GenAI Foundations Achievement Sample Questions (Q43-Q48):

NEW QUESTION # 43
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. Self-supervised Learning
  • B. Transfer Learning
  • C. Adversarial Training
  • D. P-Tuning

Answer: D

Explanation:
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.


NEW QUESTION # 44
A business wants to protect user data while using Generative Al.
What should they prioritize?

  • A. Customer feedback
  • B. Product innovation
  • C. Robust security measures
  • D. Marketing strategies

Answer: C

Explanation:
When a business is using Generative AI and wants to ensure the protection of user data, the top priority should be robust security measures. This involves implementing comprehensive data protection strategies, such as encryption, access controls, and secure data storage, to safeguard sensitive information against unauthorized access and potential breaches.
The Official Dell GenAI Foundations Achievement document underscores the importance of security in AI systems. It highlights that while Generative AI can provide significant benefits, it is crucial to maintain the confidentiality, integrity, and availability of user data12. This includes adhering to best practices for data security and privacy, which are essential for building trust and ensuring compliance with regulatory requirements.
Customer feedback (Option OA), product innovation (Option OB), and marketing strategies (Option OC) are important aspects of business operations but do not directly address the protection of user data. Therefore, the correct answer is D. Robust security measures, as they are fundamental to the ethical and responsible use of AI technologies, especially when handling sensitive user data.


NEW QUESTION # 45
A team is analyzing the performance of their Al models and noticed that the models are reinforcing existing flawed ideas.
What type of bias is this?

  • A. Systemic Bias
  • B. Confirmation Bias
  • C. Linguistic Bias
  • D. Data Bias

Answer: A

Explanation:
When AI models reinforce existing flawed ideas, it is typically indicative of systemic bias. This type of bias occurs when the underlying system, including the data, algorithms, and other structural factors, inherently favors certain outcomes or perspectives. Systemic bias can lead to the perpetuation of stereotypes, inequalities, or unfair practices that are present in the data or processes used to train the model.
The Official Dell GenAI Foundations Achievement document likely covers various types of biases and their impacts on AI systems. It would discuss how systemic bias affects the performance and fairness of AI models and the importance of identifying and mitigating such biases to increase the trust of humans over machines123. The document would emphasize the need for a culture that actively seeks to reduce bias and ensure ethical AI practices.
Confirmation Bias (Option OB) refers to the tendency to process information by looking for, or interpreting, information that is consistent with one's existing beliefs. Linguistic Bias (Option OC) involves bias that arises from the nuances of language used in the data. Data Bias (Option OD) is a broader term that could encompass various types of biases in the data but does not specifically refer to the reinforcement of flawed ideas as systemic bias does. Therefore, the correct answer is A. Systemic Bias.


NEW QUESTION # 46
What strategy can an organization implement to mitigate bias and address a lack of diversity in technology?

  • A. Ignore the issue and hope it resolves itself over time.
  • B. Reduce diversity across technology teams and roles.
  • C. Limit partnerships with nonprofits and nongovernmental organizations.
  • D. Partner with nonprofit organizations, customers, and peer companies on coalitions, advocacy groups, and public policy initiatives.

Answer: D

Explanation:
Partnerships with Nonprofits: Collaborating with nonprofit organizations can provide valuable insights and resources to address diversity and bias in technology. Nonprofits often have expertise in advocacy and community engagement, which can help drive meaningful change.


NEW QUESTION # 47
You are tasked with creating a model that uses a competitive setting between two neural networks to create new data.
Which model would you use?

  • A. Feedforward Neural Networks
  • B. Transformers
  • C. Variational Autoencoders (VAEs)
  • D. Generative Adversarial Networks (GANs)

Answer: D

Explanation:
Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, the generator and the discriminator, which are trained simultaneously through a competitive process. The generator creates new data instances, while the discriminator evaluates them against real data, effectively learning to generate new content that is indistinguishable from genuine data.
The generator's goal is to produce data that is so similar to the real data that the discriminator cannot tell the difference, while the discriminator's goal is to correctly identify whether the data it reviews is real (from the actual dataset) or fake (created by the generator). This competitive process results in the generator creating highly realistic data.
The Official Dell GenAI Foundations Achievement document likely includes information on GANs, as they are a significant concept in the field of artificial intelligence and machine learning, particularly in the context of generative AI12. GANs have a wide range of applications, including image generation, style transfer, data augmentation, and more.
Feedforward Neural Networks (Option OA) are basic neural networks where connections between the nodes do not form a cycle. Variational Autoencoders (VAEs) (Option OB) are a type of autoencoder that provides a probabilistic manner for describing an observation in latent space. Transformers (Option OD) are a type of model that uses self-attention mechanisms and is widely used in natural language processing tasks. While these are all important models in AI, they do not use a competitive setting between two networks to create new data, making Option OC the correct answer.


NEW QUESTION # 48
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