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EMC Dell GenAI Foundations Achievement Sample Questions (Q31-Q36):

NEW QUESTION # 31
What are the potential impacts of Al in business? (Select two)

A. Reducing production and operating costs
B. Increasing the need for human intervention
C. Improving operational efficiency and enhancing customer experiences
D. Limiting the use of data analytics

Answer: A,C

Explanation:
Reducing Costs: AI can automate repetitive and time-consuming tasks, leading to significant cost savings in production and operations. By optimizing resource allocation and minimizing errors, businesses can lower their operating expenses.

NEW QUESTION # 32
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. Variational Autoencoders (VAEs)
B. Feedforward Neural Networks
C. Transformers
D. Generative Adversarial Networks (GANs)

Answer: D

Explanation:
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.

NEW QUESTION # 33
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 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.
C. Supervised learning is common for fine tuning and customization, while unsupervised learning is common for base model training.
D. Supervised learning is common for base model training, while unsupervised learning is common for fine tuning and customization.

Answer: B

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 # 34
What is feature-based transfer learning?

A. Selecting specific features of a model to keep while removing others
B. Transferring the learning process to a new model
C. Enhancing the model's features with real-time data
D. Training a model on entirely new features

Answer: A

Explanation:
Feature-based transfer learning involves leveraging certain features learned by a pre-trained model and adapting them to a new task. Here's a detailed explanation:
Feature Selection:This process involves identifying and selecting specific features or layers from a pre-trained model that are relevant to the new task while discarding others that are not.
Adaptation:The selected features are then fine-tuned or re-trained on the new dataset, allowing the model to adapt to the new task with improved performance.
Efficiency:This approach is computationally efficient because it reuses existing features, reducing the amount of data and time needed for training compared to starting from scratch.
References:
Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359.
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How Transferable Are Features in Deep Neural Networks? In Advances in Neural Information Processing Systems.

NEW QUESTION # 35
What is the primary function of Large Language Models (LLMs) in the context of Natural Language Processing?

A. LLMs receive input in human language and produce output in human language.
B. LLMs are used to shrink the size of the neural network.
C. LLMs are used to increase the size of the neural network.
D. LLMs are used to parse image, audio, and video data.

Answer: A

Explanation:
The primary function of Large Language Models (LLMs) in Natural Language Processing (NLP) is to process and generate human language. Here's a detailed explanation:
Function of LLMs:LLMs are designed to understand, interpret, and generate human language text.
They can perform tasks such as translation, summarization, and conversation.
Input and Output:LLMs take input in the form of text and produce output in text, making them versatile tools for a wide range of language-based applications.
Applications:These models are used in chatbots, virtual assistants, translation services, and more, demonstrating their ability to handle natural language efficiently.
References:
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D.
(2020). Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems.

NEW QUESTION # 36
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Dan Wells Dan Wells

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