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1Z0-1122-25考試題庫，Oracle 1Z0-1122-25最新考古題

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Oracle 1Z0-1122-25 考試大綱：

主題
簡介

主題 1

Get started with OCI AI Portfolio: This section measures the proficiency of Cloud AI Specialists in exploring Oracle Cloud Infrastructure (OCI) AI services. It provides an overview of OCI AI and machine learning services, details AI infrastructure capabilities and explains responsible AI principles to ensure ethical and transparent AI development.

主題 2

Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.

主題 3

Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.

主題 4

Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.

主題 5

Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.

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最新的 Oracle Cloud Infrastructure 1Z0-1122-25 免費考試真題 (Q16-Q21):

問題 #16
Which is NOT a capability of OCI Vision's image analysis?

A. Assigning classification labels to images
B. Locating and extracting text in images
C. Translating text in images to another language
D. Object detection with bounding boxes

答案：C

解題說明：
OCI Vision's image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision's image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision's core image analysis features.
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問題 #17
What key objective does machine learning strive to achieve?

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

答案：A

解題說明：
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.

問題 #18
What are Convolutional Neural Networks (CNNs) primarily used for?

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

答案：C

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 #19
In machine learning, what does the term "model training" mean?

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

答案：D

解題說明：
In machine learning, "model training" refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

問題 #20
What is the benefit of using embedding models in OCI Generative AI service?

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

答案：B

解題說明：
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 .

問題 #21
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