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1Z0-1122-25独学書籍 & 1Z0-1122-25トレーニング

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Oracle Cloud Infrastructure 2025 AI Foundations Associate 認定 1Z0-1122-25 試験問題 (Q36-Q41):

質問 # 36
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 is ideal for tasks such as text-to-speech conversion.
C. It provides a cost-effective solution for simple AI tasks.
D. It offers seamless integration with social media platforms.

正解：A

解説：
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.

質問 # 37
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.

正解：A

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 # 38
What role do Transformers perform in Large Language Models (LLMs)?

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

正解：B

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 # 39
What is the primary benefit of using the OCI Language service for text analysis?

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

正解：B

解説：
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.

質問 # 40
How does AI enhance human efforts?

A. By completely replacing human workers in all tasks
B. By deleting data humans need to handle
C. By increasing the physical strength of humans
D. By processing data at a speed and effectiveness far beyond human capability

正解：D

解説：
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI's ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.

質問 # 41
......

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そして訪れた二男十一歳の冬、そ寒空の下の公園は静かだった、仕事に忙しいから試験の準備をする時間はあまりないとしたら、絶対MogiExamの1Z0-1122-25問題集を見逃すことはできないです、さらに、通常のお客様になると、より多くの会員割引とOracle Cloud Infrastructure 2025 AI Foundations Associate優待サービスをお楽しみいただけます。

有効的な1Z0-1122-25日本語復習赤本試験-試験の準備方法-最高の1Z0-1122-25独学書籍

MogiExamの1Z0-1122-25 問題集はあなたが1Z0-1122-25認定試験に準備するときに最も欠かせない資料です、1Z0-1122-25練習問題を買いたい場合、自分のメールアドレスを記入してください、そして、Oracleサイバースペースであらゆる種類の情報を今すぐ入手できることは間違いありません。

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1Z0-1122-25日本語復習赤本 & 1Z0-1122-25独学書籍

1Z0-1122-25独学書籍 & 1Z0-1122-25トレーニング

Oracle Cloud Infrastructure 2025 AI Foundations Associate 認定 1Z0-1122-25 試験問題 (Q36-Q41):

有効的な1Z0-1122-25日本語復習赤本試験-試験の準備方法-最高の1Z0-1122-25独学書籍

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