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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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最新的 Oracle Cloud Infrastructure 1z0-1122-26 免費考試真題 (Q30-Q35):

問題 #30
Which is NOT a capability of OCI Vision ' s image analysis?

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

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 #31
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

A. It offers seamless integration with social media platforms.
B. It provides a cost-effective solution for simple AI tasks.
C. It delivers exceptional performance and scalability for complex AI tasks.
D. It is ideal for tasks such as text-to-speech conversion.

答案：C

問題 #32
What is the purpose of the model catalog in OCI Data Science?

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

答案：D

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 #33
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

A. Document Understanding
B. Vision
C. Speech
D. Language

答案：C

解題說明：
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.

問題 #34
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

答案：A

解題說明：
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.

問題 #35
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