1z0-1122-26資訊 & 1z0-1122-26熱門題庫

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Artificial Intelligence and Machine Learning Fundamentals	- Artificial Intelligence Concepts 1. AI use cases and applications 2. AI fundamentals and terminology - Machine Learning Fundamentals 1. Supervised and unsupervised learning 2. Machine learning models and architectures - Deep Learning Fundamentals 1. Convolutional and sequence models 2. Neural networks
OCI Generative AI and Oracle Database AI Capabilities	- OCI Generative AI 1. OCI Generative AI services 2. Generative AI models and applications - Oracle AI Database 1. AI capabilities in Oracle Database 2. Vector database concepts - AI Application Frameworks 1. Retrieval and AI application concepts 2. Language frameworks
Oracle AI and Machine Learning Services	- OCI AI Services 1. Vision 2. Speech 3. Language 4. Document Understanding - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI - Oracle AI Stack 1. AI data and machine learning services 2. AI infrastructure
Generative AI and Large Language Models	- Large Language Models 1. LLM fundamentals 2. Language models and generative AI applications - Generative AI Fundamentals 1. Generative AI concepts and capabilities 2. Generative AI use cases

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最新的 Oracle Cloud Infrastructure 1z0-1122-26 免費考試真題 (Q34-Q39):

問題 #34
What is the primary benefit of using the OCI Language service for text analysis?

A. It requires extensive machine learning expertise to use.
B. It allows for text analysis at scale without machine learning expertise.
C. It provides image processing capabilities.
D. It only works with structured data.

答案：B

解題說明：
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

問題 #35
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. Unsupervised learning
C. Supervised learning
D. Active learning

答案：B

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 #36
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

A. Zero-shot Prompting
B. Retrieval-Augmented Generation (RAG)
C. Prompt Engineering
D. Reinforcement Learning

答案：B

解題說明：
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.

問題 #37
What is the purpose of the model catalog in OCI Data Science?

A. To create and switch between different environments
B. To deploy models as HTTP endpoints
C. To store, track, share, and manage models
D. To provide a preinstalled open source library

答案：C

解題說明：
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.

問題 #38
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

A. To detect specific features in the input image.
B. To generate new images.
C. To classify the input image.
D. To reduce the spatial dimensions of the input image.

答案：A

解題說明：
A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.

問題 #39
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