Pass Guaranteed 2025 1Z0-1122-25: Useful Reliable Oracle Cloud Infrastructure 2025 AI Foundations Associate Test Sample
Pass Guaranteed 2025 1Z0-1122-25: Useful Reliable Oracle Cloud Infrastructure 2025 AI Foundations Associate Test Sample
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Tags: Reliable 1Z0-1122-25 Test Sample, 1Z0-1122-25 Lab Questions, Most 1Z0-1122-25 Reliable Questions, Simulations 1Z0-1122-25 Pdf, New 1Z0-1122-25 Test Book
These Oracle 1Z0-1122-25 practice tests simulate the real Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) exam pattern, track your progress, and help you overcome mistakes. Our Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) desktop software is compatible with Windows. Whereas, the web-based Oracle 1Z0-1122-25 Practice Exam works online on iOS, Linux, Android, Windows, and Mac. Additionally, the web-based Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) practice exam is also compatible with MS Edge, Internet Explorer, Opera, Firefox, Safari, and Chrome.
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1Z0-1122-25 Lab Questions - Most 1Z0-1122-25 Reliable Questions
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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q24-Q29):
NEW QUESTION # 24
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Supervised learning
- B. Active learning
- C. Reinforcement learning
- D. Unsupervised learning
Answer: D
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 # 25
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Doubling the neural network layers
- B. Upgrading the hardware of the AI clusters
- C. Encrypting the data for security reasons
- D. Adjusting the model parameters to improve accuracy
Answer: D
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
NEW QUESTION # 26
What key objective does machine learning strive to achieve?
- A. Explicitly programming computers
- B. Creating algorithms to solve complex problems
- C. Improving computer hardware
- D. Enabling computers to learn and improve from experience
Answer: D
Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.
NEW QUESTION # 27
What is the purpose of the model catalog in OCI Data Science?
- A. To deploy models as HTTP endpoints
- B. To store, track, share, and manage models
- C. To provide a preinstalled open source library
- D. To create and switch between different environments
Answer: B
Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
NEW QUESTION # 28
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?
- A. Multi-Class Classification
- B. Binary Classification
- C. Clustering
- D. Regression
Answer: A
Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
NEW QUESTION # 29
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