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Valid D-GAI-F-01 Exam Dumps Materials - D-GAI-F-01 Quiz Cram - ExamDumpsVCE

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EMC Dell GenAI Foundations Achievement Sample Questions (Q49-Q54):

NEW QUESTION # 49
What is a principle thatguides organizations, government, and developers towards the ethical use of Al?

A. Al models must ensure data privacy and confidentiality.
B. Only regulatory agencies should be held accountable for the accuracy, fairness, and use of Al models
C. The value of Al models must only be measured in financial gain.
D. Al models must always agree with the user's point of view.

Answer: A

Explanation:
One of the guiding principles for the ethical use of AI is ensuring data privacy and confidentiality. Here's a detailed explanation:
Ethical Principle:
Explanation:Organizations, governments, and developers are increasingly recognizing the importance of protecting individuals' data. Ensuring data privacy and confidentiality is crucial to maintaining trust and compliance with legal standards.
Implementation:AI models must be designed to handle data responsibly, employing techniques such as encryption, anonymization, and secure data storage to protect sensitive information.
Regulatory Compliance:Adhering to regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) is essential for legal and ethical AI deployment.
References:
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines.
Nature Machine Intelligence, 1(9), 389-399.
Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083),
20160360.

NEW QUESTION # 50
A company is developing an Al strategy.
What is a crucial part of any Al strategy?

A. Data management
B. Customer service
C. Product design
D. Marketing

Answer: A

Explanation:
Data management is a critical component of any AI strategy. It involves the organization, storage, and maintenance of data in a way that ensures its quality, security, and accessibility for AI systems. Effective data management is essential because AI models rely on data to learn and make predictions. Without well-managed data, AI systems cannot function correctly or efficiently.
The Official Dell GenAI Foundations Achievement document likely covers the importance of data management in AI strategies. It would discuss how a robust AI ecosystem requires high-quality data, which is foundational for training accurate and reliable AI models1. The document would also emphasize the role of data management in addressing challenges related to the application of AI, such as ensuring data privacy, mitigating biases, and maintaining data integrity1.
While marketing (Option OA), customer service (Option OB), and product design (Option OD) are important aspects of a business that can be enhanced by AI, they are not as foundational to the AI strategy itself as data management. Therefore, the correct answer is C. Data management, as it is crucial for the development and implementation of AI systems.

NEW QUESTION # 51
What is the difference between supervised and unsupervised learning in the context of training Large Language Models (LLMs)?

A. Supervised learning feeds a large corpus of raw data into the Al system, while unsupervised learning uses labeled data to teach the Al system what output is expected.
B. Supervised learning is common for fine tuning and customization, while unsupervised learning is common for base model training.
C. Supervised learning uses labeled data to teach the Al system what output is expected, while unsupervised learning feeds a large corpus of raw data into the Al system, which determines the appropriate weights in its neural network.
D. Supervised learning is common for base model training, while unsupervised learning is common for fine tuning and customization.

Answer: C

Explanation:
Supervised Learning: Involves using labeled datasets where the input-output pairs are provided. The AI system learns to map inputs to the correct outputs by minimizing the error between its predictions and the actual labels.

NEW QUESTION # 52
Why should artificial intelligence developers always take inputs from diverse sources?

A. To investigate the model requirements properly
B. To determine where and how the dataset is produced
C. To perform exploratory data analysis
D. To cover all possible cases that the model should handle

Answer: D

Explanation:
Diverse Data Sources: Utilizing inputs from diverse sources ensures the AI model is exposed to a wide range of scenarios, dialects, and contexts. This diversity helps the model generalize better and avoid biases that could occur if the data were too homogeneous.

NEW QUESTION # 53
A company is planning its resources for the generative Al lifecycle.
Which phase requires the largest amount of resources?

A. Inferencing
B. Fine-tuning
C. Deployment
D. Training

Answer: D

Explanation:
The training phase of the generative AI lifecycle typically requires the largest amount of resources. This is because training involves processing large datasets to create models that can generate new data or predictions.
It requires significant computational power and time, especially for complex models such as deep learning neural networks. The resources needed include data storage, processing power (often using GPUs or specialized hardware), and the time required for the model to learn from the data.
In contrast, deployment involves implementing the model into a production environment, which, while important, often does not require as much resource intensity as the training phase. Inferencing is the process where the trained model makes predictions, which does require resources but not to the extent of the training phase. Fine-tuning is a process of adjusting a pre-trained model to a specific task, which also uses fewer resources compared to the initial training phase.
The Official Dell GenAI Foundations Achievement document outlines the importance of understanding the concepts of artificial intelligence, machine learning, and deep learning, as well as the scope and need of AI in business today, which includes knowledge of the generative AI lifecycle1.

NEW QUESTION # 54
......

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