PASS GUARANTEED 2025 ACCURATE EMC VALUABLE D-GAI-F-01 FEEDBACK

Pass Guaranteed 2025 Accurate EMC Valuable D-GAI-F-01 Feedback

Pass Guaranteed 2025 Accurate EMC Valuable D-GAI-F-01 Feedback

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EMC Dell GenAI Foundations Achievement Sample Questions (Q51-Q56):

NEW QUESTION # 51
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. Partner with nonprofit organizations, customers, and peer companies on coalitions, advocacy groups, and public policy initiatives.
  • C. Limit partnerships with nonprofits and nongovernmental organizations.
  • D. Reduce diversity across technology teams and roles.

Answer: B

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 # 52
A tech company is developing ethical guidelines for its Generative Al.
What should be emphasized in these guidelines?

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

Answer: A

Explanation:
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.


NEW QUESTION # 53
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. Generator
  • B. Decoder
  • C. Discriminator
  • D. Encoder

Answer: C

Explanation:
In a Generative Adversarial Network (GAN), the network that evaluates whether the data generated by the other network is real or fake is called the Discriminator. The GAN architecture consists of two main components: the Generator and the Discriminator. The Generator's role is to create data that is similar to the real data, while the Discriminator's role is to evaluate the data and determine if it is real (from the actual dataset) or fake (created by the Generator). The Discriminator learns to make this distinction through training, where it is presented with both real and generated data1.
This setup creates a competitive environment where the Generator improves its ability to create realistic data, and the Discriminator improves its ability to detect fakes. This adversarial process enhances the quality of the generated data over time, making GANs powerful tools for generating new data instances that are indistinguishable from real data1.
The terms "Decoder" (Option OB) and "Encoder" (Option OD) are associated with different types of neural network architectures, such as autoencoders, and do not describe the evaluating network in a GAN. The
"Generator" (Option OA) is the part of the GAN that creates data, not the part that evaluates it. Therefore, the correct answer is C. Discriminator, as it is the network within a GAN that is responsible for evaluating the authenticity of the generated data1.


NEW QUESTION # 54
What is the primary purpose oi inferencing in the lifecycle of a Large Language Model (LLM)?

  • A. To use the model in a production, research, or test environment
  • B. To randomize all the statistical weights of the neural networks
  • 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: A

Explanation:
Inferencing in the lifecycle of a Large Language Model (LLM) refers to using the model in practical applications. Here's an in-depth explanation:
Inferencing:This is the phase where the trained model is deployed to make predictions or generate outputs based on new input data. It is essentially the model's application stage.
Production Use:In production, inferencing involves using the model in live applications, such as chatbots or recommendation systems, where it interacts with real users.
Research and Testing:During research and testing, inferencing is used to evaluate the model's performance, validate its accuracy, and identify areas for improvement.
References:
LeCun, Y., Bengio, Y., & Hinton, G. (2015).Deep Learning. Nature, 521(7553), 436-444.
Chollet, F. (2017). Deep Learning with Python. Manning Publications.


NEW QUESTION # 55
Imagine a company wants to use Al to improve its customer service by generating personalized responses to customer inquiries.
Which type of Al would be most suitable for this task?

  • A. Generative Al
  • B. Storage Al
  • C. Sorting Al
  • D. Analytical Al

Answer: A

Explanation:
Generative AI is the most suitable type of artificial intelligence for generating personalized responses to customer inquiries. This category of AI focuses on creating content, whether it be text, images, or other forms of media, that is similar to data it has been trained on. In the context of customer service, Generative AI can be used to develop chatbots or virtual assistants that provide users with immediate, relevant, and personalized communication.
The Official Dell GenAI Foundations Achievement document likely discusses the capabilities of Generative AI in the context of business applications, including customer service. It would explain how Generative AI can improve customer interactions by providing advanced analytics, hyper-personalized offerings, and support through natural-language interactions1. This aligns with the goal of enhancing customer service through AI-driven personalization.
Analytical AI (Option OB) typically refers to AI that analyzes data and provides insights, which is crucial for decision-making but not directly related to generating responses. Sorting AI (Option OC) and Storage AI (Option OD) are not standard categories within AI and do not specifically pertain to the task of generating personalized content. Therefore, the correct answer is A. Generative AI, as it is designed to generate new content that can mimic human-like interactions, making it ideal for personalized customer service applications.


NEW QUESTION # 56
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