1Z0-1122-25 NEW BRAINDUMPS FILES & 1Z0-1122-25 PRACTICE ONLINE

1Z0-1122-25 New Braindumps Files & 1Z0-1122-25 Practice Online

1Z0-1122-25 New Braindumps Files & 1Z0-1122-25 Practice Online

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q19-Q24):

NEW QUESTION # 19
What is the primary purpose of reinforcement learning?

  • A. Learning from outcomes to make decisions
  • B. Making predictions from labeled data
  • C. Finding relationships within data sets
  • D. Identifying patterns in data

Answer: A

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


NEW QUESTION # 20
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

  • A. Providing labels for the output neurons
  • B. Directly predicting the final output
  • C. Storing the input pixel values
  • D. Capturing the internal representation of the raw image data

Answer: D

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


NEW QUESTION # 21
What is the benefit of using embedding models in OCI Generative AI service?

  • A. They simplify managing databases.
  • B. They optimize the use of computational resources.
  • C. They facilitate semantic searches.
  • D. They enable creating detailed graphics.

Answer: C

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for "financial reports," an embedding model can understand that "quarterly earnings" is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .


NEW QUESTION # 22
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

Answer: B

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


NEW QUESTION # 23
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

  • A. Reinforcement learning
  • B. Active learning
  • C. Unsupervised learning
  • D. Supervised learning

Answer: C

Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .


NEW QUESTION # 24
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