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Oracle 1z0-1122-24 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.
トピック 2	Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
トピック 3	Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
トピック 4	Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
トピック 5	OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate 認定 1z0-1122-24 試験問題 (Q26-Q31):

質問 # 26
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Embedding models
B. Translation models
C. Chat models
D. Generation models

正解：B

解説：
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.

質問 # 27
What is the purpose of Attention Mechanism in Transformer architecture?

A. Weigh the importance of different words within a sequence and understand the context.
B. Apply a specific function to each word individually.
C. Break down a sentence into smaller pieces called tokens.
D. Convert tokens into numerical forms (vectors) that the model can understand.

正解：A

解説：
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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質問 # 28
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

A. They are free of charge for all users.
B. They allow access to unlimited database resources.
C. They provide high performance compute resources for fine-tuning tasks.
D. They provide faster internet connection speeds.

正解：C

解説：
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.

質問 # 29
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Respect for human autonomy
B. Fairness
C. Prevention of harm
D. Explicability

正解：D

質問 # 30
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. Supervised learning
C. Unsupervised learning
D. Active learning

正解：C

解説：
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 .

質問 # 31
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