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質問 # 25
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Weigh the importance of different words within a sequence and understand the context.
- B. Convert tokens into numerical forms (vectors) that the model can understand.
- C. Apply a specific function to each word individually.
- D. Break down a sentence into smaller pieces called tokens.
正解：A
解説：
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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質問 # 26
Which is NOT a capability of OCI Vision's image analysis?
- A. Assigning classification labels to images
- B. Locating and extracting text in images
- C. Object detection with bounding boxes
- D. Translating text in images to another language
正解：D
解説：
OCI Vision's image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision's image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision's core image analysis features.
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質問 # 27
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Unsupervised learning
- B. Reinforcement learning
- C. Supervised learning
- D. Active learning
正解：A
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 # 28
Which feature is NOT available as part of OCI Speech capabilities?
- A. Supports multiple languages including English, Spanish, and Portuguese
- B. Provides timestamped, grammatically accurate transcriptions
- C. Transcribes audio and video files into text
- D. Uses extensive data science experience to operate
正解：D
解説：
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs. These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.
質問 # 29
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Sentiment Analysis
- B. Language Detection
- C. Text Generation
- D. Text Classification
正解：C
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 # 30
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 creates input prompts, while Fine-tuning retrains the model on specific data.
- C. Both involve retraining the model, but Prompt Engineering does it more often.
- D. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
正解：B
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 # 31
What key objective does machine learning strive to achieve?
- A. Enabling computers to learn and improve from experience
- B. Creating algorithms to solve complex problems
- C. Improving computer hardware
- D. Explicitly programming computers
正解：A
解説：
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.
質問 # 32
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Respect for human autonomy
- B. Prevention of harm
- C. Fairness
- D. Explicability
正解：D
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 # 33
What is the primary purpose of reinforcement learning?
- A. Learning from outcomes to make decisions
- B. Identifying patterns in data
- C. Finding relationships within data sets
- D. Making predictions from labeled data
正解：A
解説：
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.
質問 # 34
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Embedding models
- B. Generation models
- C. Translation models
- D. Chat models
正解：C
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 # 35
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. DL is a subset of AI, and ML is a subset of DL.
- B. AI, ML, and DL are entirely separate fields with no overlap.
- C. AI is a subset of DL, which is a subset of ML.
- D. ML is a subset of AI, and DL is a subset of ML.
正解：D
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 # 36
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Directly predicting the final output
- B. Storing the input pixel values
- C. Capturing the internal representation of the raw image data
- D. Providing labels for the output neurons
正解：C
解説：
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
質問 # 37
Which AI domain can be employed for identifying patterns in images and extract relevant features?
- A. Speech Processing
- B. Computer Vision
- C. Anomaly Detection
- D. Natural Language Processing
正解：B
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 # 38
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Respect for human autonomy
- B. Prevention of harm
- C. Fairness
- D. Explicability
正解：D
質問 # 39
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 # 40
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Detecting vehicle number plates to issue speed citations
- B. Detecting and preventing fraud in financial transactions
- C. Analyzing historical data for unusual patterns
- D. Generating realistic images from text
正解：A
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 # 41
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