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質問 # 24
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Explicability
- B. Respect for human autonomy
- C. Fairness
- D. Prevention of harm
正解：A
質問 # 25
What is the difference between classification and regression in Supervised Machine Learning?
- A. Classification and regression both assign data points to categories.
- B. Classification assigns data points to categories, whereas regression predicts continuous values.
- C. Classification and regression both predict continuous values.
- D. Classification predicts continuous values, whereas regression assigns data points to categories.
正解：B
解説：
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".
質問 # 26
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. AI, ML, and DL are entirely separate fields with no overlap.
- B. DL is a subset of AI, and ML is a subset of DL.
- C. ML is a subset of AI, and DL is a subset of ML.
- D. AI is a subset of DL, which is a subset of ML.
正解：C
解説：
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.
質問 # 27
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It recognizes and extracts text from a document.
- B. It enhances the visual quality of documents.
- C. It provides real-time translation of text.
- D. It converts audio files into text.
正解：A
解説：
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.
質問 # 28
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
- B. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
- C. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
- D. Both involve retraining the model, but Prompt Engineering does it more often.
正解：C
解説：
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model's internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
Fine-tuning, on the other hand, refers to the process of retraining a pretrained model on a smaller, task-specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively "specializing" the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.
質問 # 29
What are Convolutional Neural Networks (CNNs) primarily used for?
- A. Image classification
- B. Text processing
- C. Image generation
- D. Time series prediction
正解：A
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 # 30
What would you use Oracle AI Vector Search for?
- A. Manage database security protocols.
- B. Store business data in a cloud database.
- C. Query data based on semantics.
- D. Query data based on keywords.
正解：C
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 # 31
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Model catalog
- C. Accelerated Data Science (ADS) SDK
- D. Notebook sessions
正解：D
解説：
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
質問 # 32
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. Manually engineer features in the data before training the model
- D. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
正解：D
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 # 33
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Doubling the neural network layers
- B. Encrypting the data for security reasons
- C. Upgrading the hardware of the AI clusters
- D. Adjusting the model parameters to improve accuracy
正解：D
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
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Analyzing historical data for unusual patterns
- B. Detecting and preventing fraud in financial transactions
- C. Generating realistic images from text
- D. Detecting vehicle number plates to issue speed citations
正解：D
解説：
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.
質問 # 35
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Translation models
- B. Generation models
- C. Embedding models
- D. Chat models
正解：A
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 # 36
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Explicability
- B. Respect for human autonomy
- C. Fairness
- D. Prevention of harm
正解：A
解説：
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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質問 # 37
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 # 38
What distinguishes Generative AI from other types of AI?
- A. Generative AI uses algorithms to predict outcomes based on past data.
- B. Generative AI involves training models to perform tasks without human intervention.
- C. Generative AI focuses on making decisions based on user interactions.
- D. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
正解：D
解説：
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.
質問 # 39
Which AI domain can be employed for identifying patterns in images and extract relevant features?
- A. Natural Language Processing
- B. Speech Processing
- C. Computer Vision
- D. Anomaly Detection
正解：C
解説：
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.
質問 # 40
In machine learning, what does the term "model training" mean?
- A. Analyzing the accuracy of a trained model
- B. Establishing a relationship between input features and output
- C. Writing code for the entire program
- D. Performing data analysis on collected and labeled data
正解：B
解説：
In machine learning, "model training" refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
質問 # 41
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