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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 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 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 AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.

トピック 4

Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.

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Oracle 1z0-1122-24試験解説 & 1z0-1122-24受験対策書

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Oracle Cloud Infrastructure 2024 AI Foundations Associate 認定 1z0-1122-24 試験問題 (Q17-Q22):

質問 # 17
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

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

正解：D

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 # 18
What role do Transformers perform in Large Language Models (LLMs)?

A. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
B. Image recognition tasks in LLMs
C. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
D. Manually engineer features in the data before training the model

正解：C

解説：
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.
This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.

質問 # 19
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

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

正解：D

質問 # 20
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Supervised learning
B. Active learning
C. Reinforcement learning
D. Unsupervised learning

正解：D

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 # 21
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?

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

正解：A

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 # 22
......

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