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| Section | Weight | Objectives |
|---|---|---|
| Intro to OCI AI Services | 20% | - OCI Language - OCI Vision - OCI Document Understanding - OCI Speech |
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals |
| Intro to Generative AI and LLMs | 15% | - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Explain Transformers Fundamentals |
| Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Basics - Discuss AI Applications and Types of Data |
| Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview |
| OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI |
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals |
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질문 # 34
What is the purpose of Attention Mechanism in Transformer architecture?
정답：C
설명：
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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질문 # 35
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
정답：D
설명：
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.
질문 # 36
What is the primary purpose of reinforcement learning?
정답：C
설명：
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.
질문 # 37
What is the benefit of using embedding models in OCI Generative AI service?
정답：D
설명：
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .
질문 # 38
What are Convolutional Neural Networks (CNNs) primarily used for?
정답：B
설명：
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.
질문 # 39
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