1z0-1122-23試験問題集を使って一日でOracle Cloud試験合格目指す（最新の30解答)
1z0-1122-23試験正確な問題集、学習ノートと理論
質問 # 16
What is the primary function of Oracle Cloud Infrastructure Speech service?
- A. Analyzing sentiment n text
- B. Transcribing spoken language into written text
- C. Recognizing objects in images
- D. Converting text into images
正解：B
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
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 # 18
What is "in-context learning" in the realm of large Language Models (LLMs)?
- A. Modifying the behavior of a pretrained LLM permanently
- B. Providing a few examples of a target task via the input prompt
- C. Teaching a mode! through zero-shot learning
- D. Training a model on a diverse range of tasks
正解：B
解説：
In-context learning is a technique that leverages the ability of large language models to learn from a few input-output examples provided in the input prompt. By conditioning on these examples, the model can infer the task and the format of the desired output, and generate a suitable response. In-context learning does not require any additional training or fine-tuning of the model, and can be used for various tasks such as text summarization, question answering, text generation, and more45. In-context learning is also known as few-shot learning or prompt-based learning. Reference: [2307.12375] In-Context Learning in Large Language Models Learns Label ...](https://arxiv.org/abs/2307.12375), [2307.07164] Learning to Retrieve In-Context Examples for Large Language Models](https://arxiv.org/abs/2307.07164)
質問 # 19
What is the difference between Large Language Models (LLMs) and traditional machine learning models?
- A. LLMs focus on image recognition tasks.
- B. LLMs are specifically designed for natural language processing and understanding.
- C. LLMs have a limited number of parameters compared to other models.
- D. LLMs require labeled output for training.
正解：B
解説：
Large language models (LLMs) are a class of deep learning models that can recognize and generate natural language, among other tasks. LLMs are trained on huge sets of text data, learning grammar, semantics, and context. LLMs use the Transformer architecture, which relies on self-attention to process and understand the input and output sequences. LLMs can perform various natural language processing and understanding tasks based on the input provided, such as text summarization, question answering, text generation, and more34. Traditional machine learning models, on the other hand, are usually trained with specific statistical algorithms that deliver pre-defined outcomes. They often require labeled data and feature engineering, and they are not as flexible and adaptable as LLMs5. Reference: What are LLMs, and how are they used in generative AI?, An Introduction to LLMOps: Operationalizing and Managing Large Language Models using Azure ML, An Introduction to Large Language Models (LLMs): How It Got ... - Labellerr
質問 # 20
In machine learning, what does the term "model training" mean?
- A. Analyzing the accuracy of a trained model
- B. Performing data analysis on collected and labeled data
- C. Writing code for the entire program
- D. Establishing a relationship between Input features and output
正解：D
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 # 21
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. Central Processing Unit (CPU)
- B. Graphics Processing Unit (GPU)
- C. Random Access Memory (RAM)
- D. Solid-State Drive (SSD)
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 # 22
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 generating spoken language from text
- D. By analyzing text sentiment
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 # 23
What is the primary purpose of Convolutional Neural Networks (CNNs)?
- A. Processing sequential data
- B. Creating music compositions
- C. Generating Images
- 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
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 # 25
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 # 26
How is Generative AI different from other AI approaches?
- A. Generative AI focuses on decision-making and optimization.
- B. Generative AI understands underlying data and creates new examples.
- C. Generative AI is used exclusively for text-based applications.
- 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
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Detecting and classifying objects in images
- B. Generating realistic Images from text
- C. Detecting and preventing fraud in financial transactions
- D. Analyzing historical data for unusual patterns
正解：A
解説：
Oracle Cloud Infrastructure Vision is a serverless, multi-tenant service, accessible using the Console, or over REST APIs. You can upload images to detect and classify objects in them. If you have lots of images, you can process them in batch using asynchronous API endpoints. Vision's features are thematically split between Document AI for document-centric images, and Image Analysis for object and scene-based images. Image Analysis supports both pretrained and custom models for object detection and image classification3. Reference: Vision - Oracle
質問 # 28
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