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最新的 Oracle Cloud Infrastructure 1Z0-1122-25 免費考試真題 (Q19-Q24):

問題 #19
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. Reinforcement learning
C. Active learning
D. Unsupervised learning

答案：D

解題說明：
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 .

問題 #20
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. Binary Classification
B. Regression
C. Clustering
D. Multi-Class Classification

答案：D

解題說明：
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.

問題 #21
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Image classification
B. Image generation
C. Time series prediction
D. Text processing

答案：A

解題說明：
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

問題 #22
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

A. It recognizes and extracts text from a document.
B. It converts audio files into text.
C. It provides real-time translation of text.
D. It enhances the visual quality of documents.

答案：A

解題說明：
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.

問題 #23
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

答案：A

解題說明：
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.

問題 #24
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