GoShikenからの試験合格準備の必需品1z0-1122-23試験トレーニング問題
有効なパス率はOracle Cloudの1z0-1122-23試験問題
質問 # 12
How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?
- A. By generating lifelike speech from text
- B. By transcribing spoken language
- C. By analyzing text sentiment
- 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 # 13
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. Random Access Memory (RAM)
- B. Solid-State Drive (SSD)
- C. Central Processing Unit (CPU)
- D. Graphics Processing Unit (GPU)
正解：D
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 # 14
What role do tokens play in Large Language Models (LLMs)?
- A. They represent the numerical values of model parameters.
- B. They are Individual units into which a piece of text is divided during processing by the model.
- C. They determine the size of the model's memory.
- D. They are used to define the architecture of the model's neural network.
正解：B
解説：
Tokens are the basic units of text representation in large language models. They can be words, subwords, characters, or symbols. Tokens are used to encode the input text into numerical vectors that can be processed by the model's neural network. Tokens also determine the vocabulary size and the maximum sequence length of the model3. Reference: Oracle Cloud Infrastructure 2023 AI Foundations Associate | Oracle University
質問 # 15
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 # 16
What is the difference between Large Language Models (LLMs) and traditional machine learning models?
- A. LLMs have a limited number of parameters compared to other models.
- B. LLMs require labeled output for training.
- C. LLMs are specifically designed for natural language processing and understanding.
- D. LLMs focus on image recognition tasks.
正解：C
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 # 17
What is the primary purpose of reinforcement learning?
- A. Making predictions from labeled data
- B. Finding relationships within data sets
- C. Learning from outcomes to make decisions
- D. Identifying patterns in data
正解：C
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 # 18
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. 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 # 19
How does Oracle Cloud Infrastructure Anomaly Detection service contribute to fraud detection?
- A. By identifying abnormal patterns in data
- B. By transcribing spoken language
- C. By analyzing text sentiment
- D. By generating spoken language from text
正解：A
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 # 20
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Apply a specific function to each word individually.
- B. Weigh the importance of different words within a sequence and understand the context.
- C. Convert tokens into numerical forms (vectors) that the model can understand.
- D. Break down a sentence into smaller pieces called tokens.
正解：B
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 # 21
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 # 22
Which Deep Learning model is well-suited for processing sequential data, such as sentences?
- A. Generative Adversarial Network (GAN)
- B. Variational Autoencoder (VAE)
- C. Convolutional Neural Network (CNN)
- 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 # 23
Which type of machine learning is used for already labeled data sets?
- A. Unsupervised earning
- B. Supervised learning
- C. Reinforcement learning
- D. Active learning
正解：B
解説：
Supervised learning is a type of machine learning that uses labeled data sets to train algorithms that can classify data or predict outcomes. Labeled data sets are data sets that have both input features and output labels for each instance. For example, a labeled data set for image classification would have images as input features and the corresponding categories (such as dog, cat, bird, etc.) as output labels. Supervised learning algorithms learn the relationship between the input features and the output labels from the training data set and then use that relationship to make predictions on new or unseen data. Supervised learning can be divided into two subtypes: classification and regression. Classification is the task of assigning discrete categories to data instances, such as spam or not spam for emails. Regression is the task of predicting continuous values for data instances, such as house prices or stock prices. Reference: : Oracle Cloud Infrastructure AI - Machine Learning Concepts, What is Supervised Learning? | IBM
質問 # 24
......
全問1z0-1122-23問題集とOracle Cloud Infrastructure 2023 AI Foundations Associateトレーニングコース：https://www.goshiken.com/Oracle/1z0-1122-23-mondaishu.html
無料テストエンジンOracle Cloud Infrastructure 2023 AI Foundations Associate認定試験：https://drive.google.com/open?id=1QE7mUZA4hz3dbNRBiRKfRC1V3jdAxCi8