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Oracle Cloud Infrastructure 2023 AI Foundations Associate Sample Questions (Q10-Q15):

NEW QUESTION # 10
What is the difference between Large Language Models (LLMs) and traditional machine learning models?

A. LLMs focus on image recognition tasks.
B. LLMs are specifically designed for natural language processing and understanding.
C. LLMs require labeled output for training.
D. LLMs have a limited number of parameters compared to other models.

Answer: B

Explanation:
Large language models (LLMs) are a class of deep learning models that can recognize and generate natural language, among other tasks. LLMs are trained on huge sets of text data, learning grammar, semantics, and context. LLMs use the Transformer architecture, which relies on self-attention to process and understand the input and output sequences. LLMs can perform various natural language processing and understanding tasks based on the input provided, such as text summarization, question answering, text generation, and more34. Traditional machine learning models, on the other hand, are usually trained with specific statistical algorithms that deliver pre-defined outcomes. They often require labeled data and feature engineering, and they are not as flexible and adaptable as LLMs5. Reference: What are LLMs, and how are they used in generative AI?, An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML, An Introduction to Large Language Models (LLMs): How It Got ... - Labellerr

NEW QUESTION # 11
In machine learning, what does the term "model training" mean?

A. Analyzing the accuracy of a trained model
B. Establishing a relationship between Input features and output
C. Performing data analysis on collected and labeled data
D. Writing code for the entire program

Answer: B

Explanation:
Model training is the process of finding the optimal values for the model parameters that minimize the error between the model predictions and the actual output. This is done by using a learning algorithm that iteratively updates the parameters based on the input features and the output1. Reference: Oracle Cloud Infrastructure Documentation

NEW QUESTION # 12
What is the primary goal of machine learning?

A. Explicitly programming computers
B. Creating algorithms to solve complex problems
C. Enabling computers to learn and improve from experience
D. Improving computer hardware

Answer: C

Explanation:
Machine learning is a branch of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed. Machine learning algorithms can adapt to new data and situations and improve their performance over time2. Reference: Artificial Intelligence (AI) | Oracle

NEW QUESTION # 13
What is the purpose of fine-tuning Large Language Models?

A. To prevent the model from overfitting
B. To reduce the number of parameters in the model
C. To specialize the model's capabilities for specific tasks
D. To Increase the complexity of the model architecture

Answer: C

Explanation:
Fine-tuning is the process of updating the model parameters on a new task and dataset, using a pre-trained large language model as the starting point. Fine-tuning allows the model to adapt to the specific context and domain of the new task, and improve its performance and accuracy. Fine-tuning can be used to customize the model's capabilities for specific tasks such as text classification, named entity recognition, and machine translation82. Fine-tuning is also known as transfer learning or task-based learning. Reference: A Complete Guide to Fine Tuning Large Language Models, Finetuning Large Language Models - DeepLearning.AI

NEW QUESTION # 14
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They ensure that the model size, training time, and data size are balanced for optimal results.
B. They disregard model size and prioritize high-quality data only.
C. They prioritize larger model sizes to achieve better performance.
D. They focus on increasing the number of tokens while keeping the model size constant.

Answer: C

Explanation:
Large language models are trained on massive amounts of data to capture the complexity and diversity of natural language. Larger model sizes mean more parameters, which enable the model to learn more patterns and nuances from the data. Larger models also tend to generalize better to new tasks and domains. However, larger models also require more computational resources, data quality, and data size to train and deploy. Therefore, large language models handle the trade-off by prioritizing larger model sizes to achieve better performance, while using various techniques to optimize the training and inference efficiency4. Reference: Artificial Intelligence (AI) | Oracle

NEW QUESTION # 15
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