お手軽にダウンロードできる1z0-1122-23試験問題集が更新されたのは30問があります
最新更新されたのは1z0-1122-23試験問題2023年更新
質問 # 16
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 # 17
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Break down a sentence into smaller pieces called tokens.
- 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. Apply a specific function to each word individually.
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 # 18
Which NVIDIA GPU is offered by Oracle Cloud Infrastructure?
- A. K80
- B. P200
- C. T4
- D. A100
正解：D
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 # 19
Which Deep Learning model is well-suited for processing sequential data, such as sentences?
- A. Variational Autoencoder (VAE)
- B. Recurrent Neural Network (RNN)
- C. Generative Adversarial Network (GAN)
- D. Convolutional Neural Network (CNN)
正解：B
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 # 20
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Reinforcement learning
- B. Active learning
- C. Unsupervised learning
- D. Supervised learning
正解：C
解説：
Unsupervised learning is a type of machine learning that is used to understand relationships within data and is not focused on making predictions or classifications. Unsupervised learning algorithms work with unlabeled data, which means the data does not have predefined categories or outcomes. The goal of unsupervised learning is to discover hidden patterns, structures, or features in the data that can provide valuable insights or reduce complexity. Some of the common techniques and applications of unsupervised learning are:
Clustering: Grouping similar data points together based on their attributes or distances. For example, clustering can be used to segment customers based on their preferences, behavior, or demographics.
Dimensionality reduction: Reducing the number of variables or features in a dataset while preserving the essential information. For example, dimensionality reduction can be used to compress images, remove noise, or visualize high-dimensional data in lower dimensions.
Anomaly detection: Identifying outliers or abnormal data points that deviate from the normal distribution or behavior of the data. For example, anomaly detection can be used to detect fraud, network intrusion, or system failure.
Association rule mining: Finding rules that describe how variables or items are related or co-occur in a dataset. For example, association rule mining can be used to discover frequent itemsets in market basket analysis or recommend products based on purchase history. Reference: : Unsupervised learning - Wikipedia, What is Unsupervised Learning? | IBM
質問 # 21
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
- A. Natural Language Processing
- B. Anomaly Detection
- C. Computer Vision
- D. Speech Processing
正解：A
解説：
Natural Language Processing (NLP) is an AI domain that is associated with tasks such as identifying the sentiment of text and translating text between languages. NLP is an interdisciplinary field that combines computer science, linguistics, and artificial intelligence to enable computers to process and understand natural language data, such as text or speech. NLP involves various techniques and applications, such as:
Text analysis: Extracting meaningful information from text data, such as keywords, entities, topics, sentiments, emotions, etc.
Text generation: Producing natural language text from structured or unstructured data, such as summaries, captions, headlines, stories, etc.
Machine translation: Translating text or speech from one language to another automatically and accurately.
Question answering: Retrieving relevant answers to natural language questions from a knowledge base or a document collection.
Speech recognition: Converting speech signals into text or commands.
Speech synthesis: Converting text into speech signals with natural sounding voices.
Natural language understanding: Interpreting the meaning and intent of natural language inputs and generating appropriate responses.
Natural language generation: Creating natural language outputs that are coherent, fluent, and relevant to the context. Reference: : What is Natural Language Processing? | IBM, Natural language processing - Wikipedia
質問 # 22
Which type of machine learning is used for already labeled data sets?
- A. Reinforcement learning
- B. Unsupervised earning
- C. Active learning
- D. Supervised learning
正解：D
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 # 23
What is the primary goal of machine learning?
- A. Explicitly programming computers
- B. Creating algorithms to solve complex problems
- C. Improving computer hardware
- D. Enabling computers to learn and improve from experience
正解：D
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 # 24
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 # 25
How can Oracle Cloud Infrastructure Document Understanding service be applied in business processes?
- A. By transcribing spoken language
- B. By generating lifelike speech from text
- C. By automating data extraction from documents
- D. By analyzing text sentiment
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 # 26
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 # 27
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 # 28
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