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質問 # 18
How does Oracle Cloud Infrastructure Anomaly Detection service contribute to fraud detection?
- A. By transcribing spoken language
- B. By identifying abnormal patterns in data
- C. By analyzing text sentiment
- D. By generating spoken language from text
正解：B
解説：
Oracle Cloud Infrastructure Anomaly Detection is an AI service that provides real-time and batch anomaly detection for univariate and multivariate time series data. Through a simple user interface, organizations can create and train models to detect anomalies and identify unusual behavior, changes in trends, outliers, and more. Anomaly Detection can contribute to fraud detection by analyzing data from various sources, such as transactions, logs, sensors, or customer behavior, and alerting users when suspicious or fraudulent activities are detected2. Reference: Anomaly Detection | Oracle
質問 # 19
As an IT manager for your company, you are responsible for migrating your company's image and video analysis workloads to Oracle Cloud Infrastructure (OCI). Your team is particularly interested in a cloud service that offers advanced computer vision capabilities, including custom model training.
Which OCI service would you consider for this purpose?
- A. OCI Document Understanding
- B. OCI Language
- C. OCI Speech
- D. OCI Vision
正解：D
解説：
OCI Vision is the best choice for migrating your company's image and video analysis workloads to Oracle Cloud Infrastructure, as it offers advanced computer vision capabilities, including custom model training. With OCI Vision, you can build your own models to detect and classify objects in images and videos, using your own data and labels. You can also use OCI Vision's pretrained models for common tasks such as face detection, face recognition, and face analysis. OCI Vision supports various file formats, such as JPG, PNG, PDF, and TIFF, and can connect to many data sources, such as Object Storage, Autonomous Transaction Processing, and InfluxDB3. Reference: Vision - Oracle
質問 # 20
Which AI domain is associated with tasks such as recognizing forces in images and classifying objects?
- A. Natural Language Processing
- B. Anomaly Detection
- C. Computer Vision
- D. Speech Processing
正解：C
解説：
Computer Vision is an AI domain that is associated with tasks such as recognizing faces in images and classifying objects. Computer vision is a field of artificial intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs, and to take actions or make recommendations based on that information. Computer vision works by applying machine learning and deep learning models to visual data, such as pixels, colors, shapes, textures, etc., and extracting features and patterns that can be used for various purposes. Some of the common techniques and applications of computer vision are:
Face recognition: Identifying or verifying the identity of a person based on their facial features.
Object detection: Locating and labeling objects of interest in an image or a video.
Object recognition: Classifying objects into predefined categories, such as animals, vehicles, fruits, etc.
Scene understanding: Analyzing the context and semantics of a visual scene, such as the location, time, weather, activity, etc.
Image segmentation: Partitioning an image into multiple regions that share similar characteristics, such as color, texture, shape, etc.
Image enhancement: Improving the quality or appearance of an image by applying filters, transformations, or corrections.
Image generation: Creating realistic or stylized images from scratch or based on some input data, such as sketches, captions, or attributes. Reference: : What is Computer Vision? | IBM, Computer vision - Wikipedia
質問 # 21
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They focus on increasing the number of tokens while keeping the model size constant.
- B. They ensure that the model size, training time, and data size are balanced for optimal results.
- C. They disregard model size and prioritize high-quality data only.
- D. They prioritize larger model sizes to achieve better performance.
正解：D
解説：
Large language models are trained on massive amounts of data to capture the complexity and diversity of natural language. Larger model sizes mean more parameters, which enable the model to learn more patterns and nuances from the data. Larger models also tend to generalize better to new tasks and domains. However, larger models also require more computational resources, data quality, and data size to train and deploy. Therefore, large language models handle the trade-off by prioritizing larger model sizes to achieve better performance, while using various techniques to optimize the training and inference efficiency4. Reference: Artificial Intelligence (AI) | Oracle
質問 # 22
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Analyzing text to extract structured information like sentiment or entities
- B. Detecting objects and scenes in Images
- C. Converting text into images
- D. Translating speech into text
正解：A
解説：
Oracle Cloud Infrastructure Language service is a cloud-based AI service for performing sophisticated text analysis at scale. It provides various capabilities to process unstructured text and extract structured information like sentiment or entities using natural language processing techniques. Some of the capabilities supported by Oracle Cloud Infrastructure Language service are:
Language Detection: Detects languages based on the provided text, and includes a confidence score.
Text Classification: Identifies the document category and subcategory that the text belongs to.
Named Entity Recognition: Identifies common entities, people, places, locations, email, and so on.
Key Phrase Extraction: Extracts an important set of phrases from a block of text.
Sentiment Analysis: Identifies aspects from the provided text and classifies each into positive, negative, or neutral polarity.
Text Translation: Translates text into the language of your choice.
Personal Identifiable Information: Identifies, classifies, and de-identifies private information in unstructured text Reference: : Language Overview - Oracle, AI Text Analysis at Scale | Oracle
質問 # 23
Which AI task involves audio generation from text?
- A. Text to speech
- B. Text summarization
- C. Speech recognition
- D. Audio recording
正解：A
解説：
Text to speech (TTS) is an AI task that involves audio generation from text. TTS is a technology that converts text into spoken audio using natural sounding voices. TTS can read aloud any text data, such as PDFs, websites, books, emails, etc., and provide an auditory format for accessing written content. TTS can be helpful for anyone who needs to listen to text data for various reasons, such as accessibility, convenience, multitasking, learning, entertainment, etc. TTS uses different techniques and models to generate speech from text data, such as:
Concatenative synthesis: Combining pre-recorded segments of human speech based on the phonetic units of the text.
Parametric synthesis: Generating speech signals from acoustic parameters derived from the text using statistical models.
Neural synthesis: Using deep neural networks to learn the mapping between text and speech features and produce high-quality speech signals.
Expressive synthesis: Adding emotions or styles to the speech output to make it more natural and engaging. Reference: : Text-to-Speech AI: Lifelike Speech Synthesis | Google Cloud, Text-to-speech synthesis - Wikipedia
質問 # 24
What is the purpose of fine-tuning Large Language Models?
- A. To prevent the model from overfitting
- B. To reduce the number of parameters in the model
- C. To Increase the complexity of the model architecture
- D. To specialize the model's capabilities for specific tasks
正解：D
解説：
Fine-tuning is the process of updating the model parameters on a new task and dataset, using a pre-trained large language model as the starting point. Fine-tuning allows the model to adapt to the specific context and domain of the new task, and improve its performance and accuracy. Fine-tuning can be used to customize the model's capabilities for specific tasks such as text classification, named entity recognition, and machine translation82. Fine-tuning is also known as transfer learning or task-based learning. Reference: A Complete Guide to Fine Tuning Large Language Models, Finetuning Large Language Models - DeepLearning.AI
質問 # 25
What is the primary purpose of Convolutional Neural Networks (CNNs)?
- A. Processing sequential data
- B. Detecting patterns in images
- C. Generating Images
- D. Creating music compositions
正解：B
解説：
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that is particularly well-suited for image recognition and processing tasks. They are made up of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layer is the core building block of a CNN, and it is where the majority of computation occurs. It requires a few components, which are input data, a filter, and a feature map. The filter is a small matrix of weights that slides over the input data and performs element-wise multiplication and summation, resulting in a feature map that represents the activation of a certain feature in the input. By applying multiple filters, the CNN can detect different patterns in the image, such as edges, shapes, colors, textures, etc. The pooling layer is used to reduce the spatial dimensionality of the feature maps, while preserving the most important information. The fully connected layer is the final layer of a CNN, and it is where the classification or regression task is performed based on the extracted features. CNNs can learn to detect complex patterns in images by adjusting their weights during training using backpropagation and gradient descent algorithms. Reference: : Convolutional neural network - Wikipedia, What are Convolutional Neural Networks? | IBM, Convolutional Neural Network (CNN) in Machine Learning
質問 # 26
How is Generative AI different from other AI approaches?
- A. Generative AI is used exclusively for text-based applications.
- B. Generative AI understands underlying data and creates new examples.
- C. Generative AI focuses on decision-making and optimization.
- D. Generative AI generates labeled outputs for training.
正解：B
解説：
Generative AI is a branch of artificial intelligence that focuses on creating new content or data based on the patterns and structure of existing data. Unlike other AI approaches that aim to recognize, classify, or predict data, generative AI aims to generate data that is realistic, diverse, and novel. Generative AI can produce various types of content, such as images, text, audio, video, software code, product designs, and more. Generative AI uses different techniques and models to learn from data and generate new examples, such as generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and foundation models. Generative AI has many applications across different domains and industries, such as art, entertainment, education, healthcare, engineering, marketing, and more. Reference: : Oracle Cloud Infrastructure AI - Generative AI, Generative artificial intelligence - Wikipedia
質問 # 27
How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?
- A. By transcribing spoken language
- B. By analyzing text sentiment
- C. By automating data extraction from documents
- D. By generating lifelike speech from text
正解：C
解説：
Oracle Cloud Infrastructure Document Understanding service is a cloud-based AI service for automating data extraction from documents. It can process various types of documents, such as invoices, receipts, contracts, forms, etc., and extract key information fields from them using optical character recognition (OCR) and natural language understanding (NLU) techniques. It can also provide confidence scores for each extracted field and enable human verification if needed. By using this service, businesses can reduce manual efforts, improve accuracy, and accelerate workflows that involve document processing. Some of the use cases for Oracle Cloud Infrastructure Document Understanding service are:
Invoice Processing: Extract invoice details, such as invoice number, date, amount, vendor name, etc., and validate them against purchase orders or contracts.
Contract Analysis: Extract contract terms, such as parties, duration, clauses, obligations, etc., and compare them with standard templates or policies.
Form Processing: Extract form fields, such as name, address, phone number, email, etc., and populate them into databases or applications. Reference: : [Document Understanding Overview - Oracle], [AI Document Understanding at Scale | Oracle]
質問 # 28
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Convert tokens into numerical forms (vectors) that the model can understand.
- B. Apply a specific function to each word individually.
- C. Break down a sentence into smaller pieces called tokens.
- D. Weigh the importance of different words within a sequence and understand the context.
正解：D
解説：
The attention mechanism in the Transformer architecture is a technique that allows the model to focus on the most relevant parts of the input and output sequences. It computes a weighted sum of the input or output embeddings, where the weights indicate how much each word contributes to the representation of the current word. The attention mechanism helps the model capture the long-range dependencies and the semantic relationships between words in a sequence12. Reference: The Transformer Attention Mechanism - MachineLearningMastery.com, Attention Mechanism in the Transformers Model - Baeldung
質問 # 29
What is the primary purpose of reinforcement learning?
- A. Identifying patterns in data
- B. Learning from outcomes to make decisions
- C. Making predictions from labeled data
- D. Finding relationships within data sets
正解：B
解説：
Reinforcement learning is a type of machine learning that is based on learning from outcomes to make decisions. Reinforcement learning algorithms learn from their own actions and experiences in an environment, rather than from labeled data or explicit feedback. The goal of reinforcement learning is to find an optimal policy that maximizes a cumulative reward over time. A policy is a rule that determines what action to take in each state of the environment. A reward is a feedback signal that indicates how good or bad an action was for achieving a desired objective. Reinforcement learning involves a trial-and-error process of exploring different actions and observing their consequences, and then updating the policy accordingly. Some of the challenges and components of reinforcement learning are:
Exploration vs exploitation: Balancing between trying new actions that might lead to higher rewards in the future (exploration) and choosing known actions that yield immediate rewards (exploitation).
Markov decision process (MDP): A mathematical framework for modeling sequential decision making problems under uncertainty, where the outcomes depend only on the current state and action, not on the previous ones.
Value function: A function that estimates the expected long-term return of each state or state-action pair, based on the current policy.
Q-learning: A popular reinforcement learning algorithm that learns a value function called Q-function, which represents the quality of taking a certain action in a certain state.
Deep reinforcement learning: A branch of reinforcement learning that combines deep neural networks with reinforcement learning algorithms to handle complex and high-dimensional problems, such as playing video games or controlling robots. Reference: : Reinforcement learning - Wikipedia, What is Reinforcement Learning? - Overview of How it Works - Synopsys
質問 # 30
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