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質問 # 13
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 generating lifelike speech from text
- D. By automating data extraction from documents
正解：D
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 # 14
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Break down a sentence into smaller pieces called tokens.
- B. Convert tokens into numerical forms (vectors) that the model can understand.
- C. Apply a specific function to each word individually.
- 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 # 15
What is the advantage of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It is ideal for tasks such as text-to-speech conversion.
- B. It delivers exceptional performance and scalability for complex AI tasks.
- C. It offers seamless integration with social media platforms.
- D. It provides a cost-effective solution for simple AI tasks.
正解：B
解説：
Oracle Cloud Infrastructure Supercluster is a cloud service that provides ultrafast cluster networking, HPC storage, and OCI Compute bare metal instances. OCI Supercluster is ideal for training generative AI, including conversational applications and diffusion models, as it can deploy up to tens of thousands of NVIDIA GPUs per cluster for much greater scalability than similar offerings from other providers. OCI Supercluster also reduces the time needed to train AI models with simple Ethernet network architecture that provides ultrahigh performance at massive scale. Additionally, OCI Supercluster offers cost savings and access to AI subject matter experts56. Reference: OCI Supercluster and AI Infrastructure | Oracle, Oracle Delivers More Choices for AI Infrastructure and General-Purpose ...
質問 # 16
What is the primary function of Oracle Cloud Infrastructure Speech service?
- A. Transcribing spoken language into written text
- B. Converting text into images
- C. Analyzing sentiment n text
- D. Recognizing objects in images
正解：A
解説：
Oracle Cloud Infrastructure Speech is an AI service that applies automatic speech recognition (ASR) technology to transform audio-based content into text. Developers can easily make API calls to integrate Speech's pretrained models into their applications. Speech can be used for accurate, text-normalized, time-stamped transcription via the console and REST APIs as well as command-line interfaces or SDKs. You can also use Speech in an OCI Data Science notebook session. With Speech, you can filter profanities, get confidence scores for both single words and complete transcriptions, and more1. Reference: Speech AI Service that Uses ASR | OCI Speech - Oracle
質問 # 17
Which NVIDIA GPU is offered by Oracle Cloud Infrastructure?
- A. P200
- B. K80
- C. A100
- D. T4
正解：C
解説：
Oracle Cloud Infrastructure offers NVIDIA A100 Tensor Core GPUs as one of the GPU options for its compute instances. The NVIDIA A100 GPU is a powerful and versatile GPU that can accelerate a wide range of AI and HPC workloads. The A100 GPU delivers up to 20x higher performance than the previous generation V100 GPU and supports features such as multi-instance GPU, automatic mixed precision, and sparsity acceleration12. The OCI Compute bare-metal BM.GPU4.8 instance offers eight 40GB NVIDIA A100 GPUs linked via high-speed NVIDIA NVLink direct GPU-to-GPU interconnects3. This instance is ideal for training large language models, computer vision models, and other complex AI tasks. Reference: Accelerated Computing and Oracle Cloud Infrastructure (OCI) - NVIDIA, Oracle Cloud Infrastructure Offers New NVIDIA GPU-Accelerated Compute ..., GPU, Virtual Machines and Bare Metal | Oracle
質問 # 18
What is the difference between classification and regression in Supervised Machine Learning?
- A. Classification and regression both assign data points to categories.
- B. Classification predicts continuous values, whereas regression assigns data points to categories.
- C. Classification assigns data points to categories, whereas regression predicts continuous values.
- D. Classification and regression both predict continuous values.
正解：C
解説：
Classification and regression are two subtypes of supervised learning in machine learning. The main difference between them is the type of output variable they deal with. Classification assigns data points to discrete categories based on some criteria or rules. For example, classifying emails into spam or not spam based on their content is a classification problem because the output variable is binary (spam or not spam). Regression predicts continuous values for data points based on their input features. For example, predicting house prices based on their size, location, amenities, etc., is a regression problem because the output variable is continuous (house price). Classification and regression use different types of algorithms and metrics to evaluate their performance. Reference: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, Classification vs Regression in Machine Learning | by ...
質問 # 19
What is the primary purpose of reinforcement learning?
- A. Making predictions from labeled data
- B. Learning from outcomes to make decisions
- C. Finding relationships within data sets
- D. Identifying patterns in data
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 # 20
Which Deep Learning model is well-suited for processing sequential data, such as sentences?
- A. Generative Adversarial Network (GAN)
- B. Convolutional Neural Network (CNN)
- C. Variational Autoencoder (VAE)
- D. Recurrent Neural Network (RNN)
正解：D
解説：
Recurrent Neural Networks (RNNs) are a type of deep learning algorithm that can process sequential data, such as sentences, speech, or time series. They are composed of recurrent units that have a loop that allows them to store information from previous inputs and pass it to the next inputs. This way, they can capture the temporal dependencies and context within a sequence. RNNs can be used for various natural language processing tasks, such as text generation, machine translation, sentiment analysis, speech recognition, etc. However, RNNs also suffer from some limitations, such as vanishing or exploding gradients, difficulty in modeling long-term dependencies, and high computational cost. Therefore, some variants and extensions of RNNs have been proposed to overcome these challenges, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional RNN (BiRNN), Attention Mechanism, etc. Reference: : [Recurrent neural network - Wikipedia], [What are Recurrent Neural Networks? | IBM], [Recurrent Neural Network (RNN) in Machine Learning]
質問 # 21
Which is an application of Generative Adversarial Networks (GANs) in the context of Generative AI?
- A. Prediction of continuous values from Input data
- B. Classification of data points into categories
- C. Generation of labeled outputs for training
- D. Creation of realistic images that resemble training data
正解：D
解説：
Generative Adversarial Networks (GANs) are a type of AI model that can generate realistic images that resemble training data. The architecture of a GAN consists of two separate neural networks that are pitted against each other in a game-like scenario. The first network, known as the generator network, tries to create fake data that looks real. The second network, known as the discriminator network, tries to distinguish between real and fake data. The generator network learns from the feedback of the discriminator network and tries to fool it by improving the quality of the fake data. The discriminator network also learns from the feedback of the generator network and tries to improve its accuracy. The process continues until the generator network produces data that is indistinguishable from the real data4. GANs can be used to create realistic images of faces, animals, landscapes, and more5. Reference: Generative models - OpenAI, Artificial Intelligence Explained: What Are Generative Adversarial ...
質問 # 22
What is the primary goal of machine learning?
- A. Explicitly programming computers
- B. Creating algorithms to solve complex problems
- C. Enabling computers to learn and improve from experience
- D. Improving computer hardware
正解：C
解説：
Machine learning is a branch of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed. Machine learning algorithms can adapt to new data and situations and improve their performance over time2. Reference: Artificial Intelligence (AI) | Oracle
質問 # 23
How does Oracle Cloud Infrastructure Anomaly Detection service contribute to fraud detection?
- A. By transcribing spoken language
- B. By analyzing text sentiment
- C. By identifying abnormal patterns in data
- D. By generating spoken language from text
正解：C
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 # 24
You are the lead developer of a Deep Learning research team, and you are tasked with improving the training speed of your deep neural networks. To accelerate the training process, you decide to leverage specialized hardware.
Which hardware component is commonly used in Deep Learning to accelerate model training?
- A. Solid-State Drive (SSD)
- B. Graphics Processing Unit (GPU)
- C. Random Access Memory (RAM)
- D. Central Processing Unit (CPU)
正解：B
解説：
A graphics processing unit (GPU) is a specialized hardware component that can perform parallel computations on large amounts of data. GPUs are widely used in deep learning to accelerate the training of deep neural networks, as they can execute many matrix operations and tensor operations simultaneously. GPUs can significantly reduce the training time and improve the performance of deep learning models compared to using CPUs alone678. Reference: Hardware Recommendations for Machine Learning / AI, New hardware offers faster computation for artificial intelligence ..., The Best Hardware for Machine Learning - ReHack, Hardware for Deep Learning Inference: How to Choose the Best One for ...
質問 # 25
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