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質問 # 24
What is the key feature of Recurrent Neural Networks (RNNs)?
- A. They have a feedback loop that allows information to persist across different time steps.
- B. They are primarily used for image recognition tasks.
- C. They do not have an internal state.
- D. They process data in parallel.
正解：A
解説：
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by "remembering" past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.
質問 # 25
What would you use Oracle AI Vector Search for?
- A. Store business data in a cloud database.
- B. Query data based on semantics.
- C. Query data based on keywords.
- D. Manage database security protocols.
正解：B
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 # 26
What is the primary benefit of using the OCI Language service for text analysis?
- A. It allows for text analysis at scale without machine learning expertise.
- B. It provides image processing capabilities.
- C. It requires extensive machine learning expertise to use.
- D. It only works with structured data.
正解：A
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 # 27
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
- A. Support Vector Machine
- B. Gradient Descent
- C. Random Forest
- D. Backpropagation
正解：D
解説：
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.
質問 # 28
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 # 29
What is the purpose of the model catalog in OCI Data Science?
- A. To deploy models as HTTP endpoints
- B. To create and switch between different environments
- C. To provide a preinstalled open source library
- D. To store, track, share, and manage models
正解：D
解説：
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
質問 # 30
Which feature is NOT available as part of OCI Speech capabilities?
- A. Transcribes audio and video files into text
- B. Provides timestamped, grammatically accurate transcriptions
- C. Supports multiple languages including English, Spanish, and Portuguese
- 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 # 31
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Upgrading the hardware of the AI clusters
- B. Doubling the neural network layers
- C. Adjusting the model parameters to improve accuracy
- D. Encrypting the data for security reasons
正解：C
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 # 32
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Analyzing historical data for unusual patterns
- B. Detecting vehicle number plates to issue speed citations
- C. Detecting and preventing fraud in financial transactions
- D. Generating realistic images from text
正解：B
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 # 33
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
- A. Natural Language Processing
- B. Natural Language Processing
- C. Anomaly Detection
- D. Computer Vision
正解：A
解説：
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.
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. Chat models
- D. Translation models
正解：D
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
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It provides a cost-effective solution for simple AI tasks.
- B. It is ideal for tasks such as text-to-speech conversion.
- C. It delivers exceptional performance and scalability for complex AI tasks.
- D. It offers seamless integration with social media platforms.
正解：C
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 # 36
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
- B. Both involve retraining the model, but Prompt Engineering does it more often.
- C. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
- D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
正解：D
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 # 37
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
- A. By automating data extraction from documents
- B. By transcribing spoken language
- C. By generating lifelike speech from documents
- D. By analyzing sentiment in text documents
正解：A
解説：
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.
質問 # 38
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Generate transcript from documents
- B. Extract text from documents
- C. Classify documents into different types
- D. Extract tables from documents
正解：A
解説：
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
質問 # 39
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Active learning
- B. Supervised learning
- C. Reinforcement learning
- D. Unsupervised learning
正解：D
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 # 40
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It converts audio files into text.
- B. It provides real-time translation of text.
- C. It enhances the visual quality of documents.
- D. It recognizes and extracts text from a document.
正解：D
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 # 41
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