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新版D-GAI-F-01題庫上線，D-GAI-F-01題庫更新資訊

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最新的 Generative AI D-GAI-F-01 免費考試真題 (Q18-Q23):

問題 #18
A business wants to protect user data while using Generative Al.
What should they prioritize?

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

答案：A

解題說明：
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.

問題 #19
A legal team is assessing the ethical issues related to Generative Al.
What is a significant ethical issue they should consider?

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

答案：A

解題說明：
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.

問題 #20
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)

答案：D

解題說明：
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.

問題 #21
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. Encoder
C. Decoder
D. Generator

答案：A

解題說明：
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.

問題 #22
What is the purpose of fine-tuning in the generative Al lifecycle?

A. To randomize all the statistical weights of the neural network
B. To put text into a prompt to interact with the cloud-based Al system
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

答案：C

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

問題 #23
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