1z0-1122-26試験の準備方法｜素敵な1z0-1122-26最新資料試験｜ユニークなOracle Cloud Infrastructure 2026 AI Foundations Associate学習指導

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Oracle AI and Machine Learning Services	- Oracle AI Stack 1. AI infrastructure 2. AI data 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
Topic 2: Artificial Intelligence and Machine Learning Fundamentals	- Deep Learning Fundamentals 1. Neural networks 2. Convolutional and sequence models - Machine Learning Fundamentals 1. Machine learning models and architectures 2. Supervised and unsupervised learning - Artificial Intelligence Concepts 1. AI fundamentals and terminology 2. AI use cases and applications
Topic 3: OCI Generative AI and Oracle Database AI Capabilities	- Oracle AI Database 1. AI capabilities in Oracle Database 2. Vector database concepts - 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: Generative AI and Large Language Models	- Large Language Models 1. Language models and generative AI applications 2. LLM fundamentals - Generative AI Fundamentals 1. Generative AI use cases 2. Generative AI concepts and capabilities

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Oracle Cloud Infrastructure 2026 AI Foundations Associate 認定 1z0-1122-26 試験問題 (Q16-Q21):

質問 # 16
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

正解：D

解説：
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.

質問 # 17
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

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

正解：A

質問 # 18
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

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

正解：D

質問 # 19
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

A. Sentiment Analysis
B. Text Generation
C. Language Detection
D. Text Classification

正解：B

解説：
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.

質問 # 20
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. AI is a subset of DL, which is a subset of ML.
B. DL is a subset of AI, and ML is a subset of DL.
C. ML is a subset of AI, and DL is a subset of ML.
D. AI, ML, and DL are entirely separate fields with no overlap.

正解：C

解説：
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
In this hierarchy:
* AI includes all methods to make machines intelligent.
* ML refers to the methods within AI that focus on learning from data.
* DL is a specialized field within ML that deals with deep neural networks.

質問 # 21
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