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Free Oracle 1Z0-1122-23 Exam Braindumps (page: 2)

Page 2 of 8
PDF with 30 Questions

In machine learning, what does the term "model training" mean?

  1. Analyzing the accuracy of a trained model
  2. Establishing a relationship between Input features and output
  3. Writing code for the entire program
  4. Performing data analysis on collected and labeled data

Answer(s): 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 output.


Reference:

Oracle Cloud Infrastructure Documentation



What is the primary goal of machine learning?

  1. Enabling computers to learn and improve from experience
  2. Explicitly programming computers
  3. Creating algorithms to solve complex problems
  4. Improving computer hardware

Answer(s): A

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 time.


Reference:

Artificial Intelligence (AI) | Oracle



What role do tokens play in Large Language Models (LLMs)?

  1. They represent the numerical values of model parameters.
  2. They are used to define the architecture of the model's neural network.
  3. They are Individual units into which a piece of text is divided during processing by the model.
  4. They determine the size of the model's memory.

Answer(s): C

Explanation:

Tokens are the basic units of text representation in large language models. They can be words, subwords, characters, or symbols. Tokens are used to encode the input text into numerical vectors that can be processed by the model's neural network. Tokens also determine the vocabulary size and the maximum sequence length of the model.


Reference:

Oracle Cloud Infrastructure 2023 AI Foundations Associate | Oracle University



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

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

Answer(s): D

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 efficiency.


Reference:

Artificial Intelligence (AI) | Oracle






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