1z0-1122-23練習テスト問題は更新された30問題あります
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質問 # 15
How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?
- A. By generating lifelike speech from text
- B. By automating data extraction from documents
- C. By analyzing text sentiment
- D. By transcribing spoken language
正解：B
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 # 16
In machine learning, what does the term "model training" mean?
- A. Performing data analysis on collected and labeled data
- B. Analyzing the accuracy of a trained model
- C. Establishing a relationship between Input features and output
- D. Writing code for the entire program
正解：C
解説：
Model training is the process of finding the optimal values for the model parameters that minimize the error between the model predictions and the actual output. This is done by using a learning algorithm that iteratively updates the parameters based on the input features and the output1. Reference: Oracle Cloud Infrastructure Documentation
質問 # 17
Which AI domain is associated with tasks such as recognizing forces in images and classifying objects?
- A. Anomaly Detection
- B. Computer Vision
- C. Natural Language Processing
- D. Speech Processing
正解：B
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 # 18
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. Translating speech into text
- D. Converting text into images
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 # 19
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. Graphics Processing Unit (GPU)
- B. Solid-State Drive (SSD)
- C. Random Access Memory (RAM)
- D. Central Processing Unit (CPU)
正解：A
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 # 20
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They prioritize larger model sizes to achieve better performance.
- B. They disregard model size and prioritize high-quality data only.
- C. They ensure that the model size, training time, and data size are balanced for optimal results.
- D. They focus on increasing the number of tokens while keeping the model size constant.
正解：A
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 # 21
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs.
Which type of supervised learning algorithm is required in this scenario?
- A. Clustering
- B. Binary Classification
- C. Multi-Class Classification
- D. Regression
正解：C
解説：
Multi-class classification is a type of supervised learning algorithm that is required in this scenario because the output variable has more than two classes. Multi-class classification is the problem of classifying instances into one of three or more classes. For example, classifying patients into low risk, moderate risk, or high risk based on their medical history and vital signs is a multi-class classification problem because each patient can only belong to one of these three classes. Multi-class classification can be solved by using various algorithms, such as decision trees, random forests, support vector machines (SVMs), k-nearest neighbors (k-NN), naive Bayes, logistic regression, neural networks, etc. Some of these algorithms can naturally handle multi-class problems, while others need to be adapted by using strategies such as one-vs-one or one-vs-rest. Reference: : Multiclass classification - Wikipedia, Multiclass Classification- Explained in Machine Learning
質問 # 22
What is the advantage of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It offers seamless integration with social media platforms.
- B. It is ideal for tasks such as text-to-speech conversion.
- C. It provides a cost-effective solution for simple AI tasks.
- D. It delivers exceptional performance and scalability for complex AI tasks.
正解：D
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 # 23
What is the primary purpose of Convolutional Neural Networks (CNNs)?
- A. Processing sequential data
- B. Generating Images
- C. Creating music compositions
- D. Detecting patterns in images
正解：D
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 # 24
Which is an application of Generative Adversarial Networks (GANs) in the context of Generative AI?
- A. Classification of data points into categories
- B. Prediction of continuous values from Input data
- C. Creation of realistic images that resemble training data
- D. Generation of labeled outputs for training
正解：C
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 # 25
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Involves post-processing model outputs and optimizing hyper parameters
- B. Trains a model from scratch
- C. Guides the model's response using predefined prompts
- D. Customizes the model architecture
正解：C
解説：
Prompt engineering is the art of designing natural language instructions or queries that can elicit the desired response from a large language model. Prompt engineering does not modify the model parameters or architecture, but rather relies on the model's existing knowledge and capabilities. Prompt engineering can be used to perform various tasks such as text generation, sentiment analysis, and code completion, by providing the model with the appropriate context, format, and constraints67. Prompt engineering is also known as zero-shot learning or query-based learning. Reference: [2211.01910] Large Language Models Are Human-Level Prompt Engineers](https://arxiv.org/abs/2211.01910), A developer's guide to prompt engineering and LLMs - The GitHub Blog
質問 # 26
What is the purpose of fine-tuning Large Language Models?
- A. To reduce the number of parameters in the model
- B. To Increase the complexity of the model architecture
- C. To specialize the model's capabilities for specific tasks
- D. To prevent the model from overfitting
正解：C
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 # 27
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