QUESTION: 13

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

To put text into a prompt to interact with the cloud-based Al system
To randomize all the statistical weights of the neural network
To customize the model for a specific task by feeding it task-specific content
To feed the model a large volume of data from a wide variety of subjects

Answer(s): C

Explanation:

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

Reference:

"Fine-tuning a pretrained model on task-specific data improves its relevance and accuracy." (Stanford University, 2020)
Process: This process refines the model's weights and parameters, allowing it to adapt from its general knowledge base to specific nuances and requirements of the new task.
"Fine-tuning adapts general AI models to specific tasks by retraining on specialized datasets." (OpenAI, 2021)
Applications: Fine-tuning is widely used in various domains, such as customizing a language model for customer service chatbots or adapting an image recognition model for medical imaging analysis.
"Fine-tuning enables models to perform specialized tasks effectively, such as customer service and medical diagnosis." (Journal of Artificial Intelligence Research, 2019)

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QUESTION: 14

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

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

Answer(s): A

Explanation:

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.

Reference:

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.

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QUESTION: 15

What is Transfer Learning in the context of Language Model (LLM) customization?

It is where you can adjust prompts to shape the model's output without modifying its underlying weights.
It is a process where the model is additionally trained on something like human feedback.
It is a type of model training that occurs when you take a base LLM that has been trained and then train it on a different task while using all its existing base weights.
It is where purposefully malicious inputs are provided to the model to make the model more resistant to adversarial attacks.

Answer(s): C

Explanation:

Transfer learning is a technique in AI where a pre-trained model is adapted for a different but related task. Here's a detailed explanation:
Transfer Learning: This involves taking a base model that has been pre-trained on a large dataset and fine-tuning it on a smaller, task-specific dataset.
Base Weights: The existing base weights from the pre-trained model are reused and adjusted slightly to fit the new task, which makes the process more efficient than training a model from scratch.

Benefits: This approach leverages the knowledge the model has already acquired, reducing the amount of data and computational resources needed for training on the new task.

Reference:

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A Survey on Deep Transfer Learning. In International Conference on Artificial Neural Networks. Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:
Long Papers).

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QUESTION: 16

What is the significance of parameters in Large Language Models (LLMs)?

Parameters are used to parse image, audio, and video data in LLMs.
Parameters are used to decrease the size of the LLMs.
Parameters are used to increase the size of the LLMs.
Parameters are statistical weights inside of the neural network of LLMs.

Answer(s): D

Explanation:

Parameters in Large Language Models (LLMs) are statistical weights that are adjusted during the training process. Here's a comprehensive explanation:
Parameters: Parameters are the coefficients in the neural network that are learned from the training data. They determine how input data is transformed into output. Significance: The number of parameters in an LLM is a key factor in its capacity to model complex patterns in data. More parameters generally mean a more powerful model, but also require more computational resources.
Role in LLMs: In LLMs, parameters are used to capture linguistic patterns and relationships, enabling the model to generate coherent and contextually appropriate language.

Reference:

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is All You Need. In Advances in Neural Information Processing Systems. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog.

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Free D-GAI-F-01 Exam Braindumps (page: 4)

QUESTION: 13

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QUESTION: 14

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QUESTION: 15

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QUESTION: 16

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