D-GAI-F-01考古題更新是通過Dell GenAI Foundations Achievement的有用材料

Tags: D-GAI-F-01考古題更新, D-GAI-F-01在線題庫, D-GAI-F-01認證考試解析, D-GAI-F-01考試大綱, D-GAI-F-01認證

你已經報名參加EMC的D-GAI-F-01認證考試了嗎？“馬上就要到考試的時間了，但是我還是沒有信心通過考試，應該怎麼辦呢？有捷徑可以讓我順利通過考試嗎？看參考書的時間也不夠了。”你現在有這樣的心情嗎？不用著急，即使考試時間快到了，也還是有機會可以好好準備考試的。你肯定想問是什麼機會了吧。它就是NewDumps的D-GAI-F-01考古題。這是一個高效率的資料，它可以在短時間內為考試做好準備。因為這個考古題的命中率非常高，只要你認真記住考古題裏面出現的問題和答案，那麼你就可以通過D-GAI-F-01考試。

EMC D-GAI-F-01 考試大綱：

主題	簡介
主題 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.
主題 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.
主題 3	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.
主題 4	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.
主題 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.

>> D-GAI-F-01考古題更新 <<

可信任的有效的D-GAI-F-01考古題更新是通過Dell GenAI Foundations Achievement考試的第一步

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最新的 Generative AI D-GAI-F-01 免費考試真題 (Q13-Q18):

問題 #13
What is P-Tuning in LLM?

A. Adjusting prompts to shape the model's output without altering its core structure
B. Preventing a model from generating malicious content
C. Punishing the model for generating incorrect answers
D. Personalizing the training of a model to produce biased outputs

答案：A

解題說明：
Definition of P-Tuning: P-Tuning is a method where specific prompts are adjusted to influence the model's output. It involves optimizing prompt parameters to guide the model's responses effectively.

問題 #14
What are the enablers that contribute towards the growth of artificial intelligence and its related technologies?

A. The development of blockchain technology and quantum computing
B. The introduction of 5G networks and the expansion of internet service provider coverage
C. The abundance of data, lower cost high-performance compute, and improved algorithms
D. The creation of the Internet and the widespread use of cloud computing

答案：C

解題說明：
Several key enablers have contributed to the rapid growth of artificial intelligence (AI) and its related technologies. Here's a comprehensive breakdown:
Abundance of Data:The exponential increase in data from various sources (social media, IoT devices, etc.) provides the raw material needed for training complex AI models.
High-Performance Compute:Advances in hardware, such as GPUs and TPUs, have significantly lowered the cost and increased the availability of high-performance computing power required to train large AI models.
Improved Algorithms:Continuous innovations in algorithms and techniques (e.g., deep learning, reinforcement learning) have enhanced the capabilities and efficiency of AI systems.
References:
LeCun, Y., Bengio, Y., & Hinton, G. (2015).Deep Learning. Nature, 521(7553), 436-444.
Dean, J. (2020). AI and Compute. Google Research Blog.

問題 #15
What is the purpose of adversarial training in the lifecycle of a Large Language Model (LLM)?

A. To customize the model for a specific task by feeding it task-specific content
B. To feed the model a large volume of data from a wide variety of subjects
C. To make the model more resistant to attacks like prompt injections when it is deployed in production
D. To randomize all the statistical weights of the neural network

答案：C

解題說明：
Adversarial training is a technique used to improve the robustness of AI models, including Large Language Models (LLMs), against various types of attacks. Here's a detailed explanation:
Definition:Adversarial training involves exposing the model to adversarial examples-inputs specifically designed to deceive the model during training.
Purpose:The main goal is to make the model more resistant to attacks, such as prompt injections or other malicious inputs, by improving its ability to recognize and handle these inputs appropriately.
Process:During training, the model is repeatedly exposed to slightly modified input data that is designed to exploit its vulnerabilities, allowing it to learn how to maintain performance and accuracy despite these perturbations.
Benefits:This method helps in enhancing the security and reliability of AI models when they are deployed in production environments, ensuring they can handle unexpected or adversarial situations better.
References:
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and Harnessing Adversarial Examples. arXiv preprint arXiv:1412.6572.
Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial Machine Learning at Scale. arXiv preprint arXiv:1611.01236.

問題 #16
What is one of the objectives of Al in the context of digital transformation?

A. To eliminate the need for data privacy
B. To reduce the need for Internet connectivity
C. To become essential to the success of the digital economy
D. To replace all human tasks with automation

答案：C

解題說明：
One of the key objectives of AI in the context of digital transformation is to become essential to the success of the digital economy. Here's an in-depth explanation:
Digital Transformation:Digital transformation involves integrating digital technology into all areas of business, fundamentally changing how businesses operate and deliver value to customers.
Role of AI:AI plays a crucial role in digital transformation by enabling automation, enhancing decision-making processes, and creating new opportunities for innovation.
Economic Impact:AI-driven solutions improve efficiency, reduce costs, and enhance customer experiences, which are vital for competitiveness and growth in the digital economy.
References:
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Westerman, G., Bonnet, D., & McAfee, A. (2014).Leading Digital: Turning Technology into Business Transformation. Harvard Business Review Press.

問題 #17
A machine learning engineer is working on a project that involves training a model using labeled data.
What type of learning is he using?

A. Supervised learning
B. Reinforcement learning
C. Unsupervised learning
D. Self-supervised learning

答案：A

解題說明：
When a machine learning engineer is training a model using labeled data, the type of learning being employed is supervised learning. In supervised learning, the model is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns to predict the output from the input data, and the goal is to minimize the difference between the predicted and actual outputs.
The Official Dell GenAI Foundations Achievement document likely covers the fundamental concepts of machine learning, including supervised learning, as it is one of the primary categories of machine learning. It would explain that supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs12. The data is known as training data, and it consists of a set of training examples. Each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). The supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
Self-supervised learning (Option OA) is a type of unsupervised learning where the system learns to predict part of its input from other parts. Unsupervised learning (Option OB) involves training a model on data that does not have labeled responses. Reinforcement learning (Option OD) is a type of learning where an agent learns to make decisions by performing actions and receiving rewards or penalties. Therefore, the correct answer is C. Supervised learning, as it directly involves the use of labeled data for training models.

問題 #18
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