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無料Oracle 1z0-1122-24: Oracle Cloud Infrastructure 2024 AI Foundations Associate日本語参考 -信頼的なJapancert 1z0-1122-24シュミレーション問題集

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>> 1z0-1122-24日本語参考 <<

1z0-1122-24シュミレーション問題集 & 1z0-1122-24問題例

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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

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 3

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.

トピック 4

Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.

トピック 5

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 6

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.

Oracle Cloud Infrastructure 2024 AI Foundations Associate 認定 1z0-1122-24 試験問題 (Q29-Q34):

質問 # 29
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

A. Generation models
B. Translation models
C. Chat models
D. Embedding 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 # 30
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Time series prediction
B. Image generation
C. Text processing
D. Image classification

正解：D

解説：
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

質問 # 31
What would you use Oracle AI Vector Search for?

A. Query data based on semantics.
B. Store business data in a cloud database.
C. Query data based on keywords.
D. Manage database security protocols.

正解：A

解説：
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .

質問 # 32
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 prioritize larger model sizes to achieve better performance.
C. They focus on increasing the number of tokens while keeping the model size constant.
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 # 33
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Upgrading the hardware of the AI clusters
B. Adjusting the model parameters to improve accuracy
C. Encrypting the data for security reasons
D. Doubling the neural network layers

正解：B

解説：
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

質問 # 34
......

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Glen Tate Glen Tate

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