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DELL D-GAI-F-01 Practice Test - Questions Answers, Page 6

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What is one of the positive stereotypes people have about Al?

A.
Al is unbiased.
A.
Al is unbiased.
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B.
Al is suitable only in manufacturing sectors.
B.
Al is suitable only in manufacturing sectors.
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C.
Al can leave humans behind.
C.
Al can leave humans behind.
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D.
Al can help businesses complete tasks around the clock 24/7.
D.
Al can help businesses complete tasks around the clock 24/7.
Answers
Suggested answer: D

Explanation:

24/7 Availability: AI systems can operate continuously without the need for breaks, which enhances productivity and efficiency. This is particularly beneficial for customer service, where AI chatbots can handle inquiries at any time.

Use Cases: Examples include automated customer support, monitoring and maintaining IT infrastructure, and processing transactions in financial services.

Business Benefits: The continuous operation of AI systems can lead to cost savings, improved customer satisfaction, and faster response times, which are critical competitive advantages.

What is artificial intelligence?

A.
The study of computer science
A.
The study of computer science
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B.
The study and design of intelligent agents
B.
The study and design of intelligent agents
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C.
The study of data analysis
C.
The study of data analysis
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D.
The study of human brain functions
D.
The study of human brain functions
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Suggested answer: B

Explanation:

Artificial intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that would normally require human intelligence. The correct answer is option B, which defines AI as 'the study and design of intelligent agents.' Here's a comprehensive breakdown:

Definition of AI: AI involves the creation of algorithms and systems that can perceive their environment, reason about it, and take actions to achieve specific goals.

Intelligent Agents: An intelligent agent is an entity that perceives its environment and takes actions to maximize its chances of success. This concept is central to AI and encompasses a wide range of systems, from simple rule-based programs to complex neural networks.

Applications: AI is applied in various domains, including natural language processing, computer vision, robotics, and more.

Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.

Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence: A Logical Approach. Oxford University Press.

You are developing a new Al model that involves two neural networks working together in a competitive setting to generate new data.

What is this model called?

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

Explanation:

Generative Adversarial Networks (GANs) are a class of artificial intelligence models that involve two neural networks, the generator and the discriminator, which work together in a competitive setting. The generator network generates new data instances, while the discriminator network evaluates them. The goal of the generator is to produce data that is indistinguishable from real data, and the discriminator's goal is to correctly classify real and generated data. This competitive process leads to the generation of new, high-quality data1.

Feedforward Neural Networks (Option OA) are basic neural networks where connections between the nodes do not form a cycle and are not inherently competitive. Transformers (Option OC) are models that use self-attention mechanisms to process sequences of data, such as natural language, for tasks like translation and text summarization. Variational Autoencoders (VAEs) (Option OD) are a type of neural network that uses probabilistic encoders and decoders for generating new data instances but do not involve a competitive setting between two networks. Therefore, the correct answer is B. Generative Adversarial Networks (GANs), as they are defined by the competitive interaction between the generator and discriminator networks2.

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

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.

A team is working on mitigating biases in Generative Al.

What is a recommended approach to do this?

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

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.

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

A team is looking to improve an LLM based on user feedback.

Which method should they use?

A.
Adversarial Training
A.
Adversarial Training
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B.
Reinforcement Learning through Human Feedback (RLHF)
B.
Reinforcement Learning through Human Feedback (RLHF)
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C.
Self-supervised Learning
C.
Self-supervised Learning
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D.
Transfer Learning
D.
Transfer Learning
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Suggested answer: B

Explanation:

Reinforcement Learning through Human Feedback (RLHF) is a method that involves training machine learning models, particularly Large Language Models (LLMs), using feedback from humans. This approach is part of a broader category of machine learning known as reinforcement learning, where models learn to make decisions by receiving rewards or penalties.

In the context of LLMs, RLHF is used to fine-tune the models based on human preferences, corrections, and feedback. This process allows the model to align more closely with human values and produce outputs that are more desirable or appropriate according to human judgment.

The Dell GenAI Foundations Achievement document likely discusses the importance of aligning AI systems with human values and the various methods to improve AI models1. RLHF is particularly relevant for LLMs used in interactive applications like chatbots, where user satisfaction is a key metric.

Adversarial Training (Option OA) is typically used to improve the robustness of models against adversarial attacks. Self-supervised Learning (Option OC) involves models learning to understand data without explicit external labels. Transfer Learning (Option D) is about applying knowledge gained in one problem domain to a different but related domain. While these methods are valuable in their own right, they are not specifically focused on integrating human feedback into the training process, making Option OB the correct answer for improving an LLM based on user feedback.

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

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.

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