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1z0-1122-24テストエンジン練習テスト問題、試験問題集
Oracle 1z0-1122-24 認定試験の出題範囲：
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質問 # 12
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
- A. Random Forest
- B. Support Vector Machine
- C. Backpropagation
- D. Gradient Descent
正解：C
解説：
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model's accuracy over time.
Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.
質問 # 13
What is the purpose of the model catalog in OCI Data Science?
- A. To create and switch between different environments
- B. To deploy models as HTTP endpoints
- C. To provide a preinstalled open source library
- D. To store, track, share, and manage models
正解：D
解説：
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
質問 # 14
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
- A. They provide high performance compute resources for fine-tuning tasks.
- B. They provide faster internet connection speeds.
- C. They allow access to unlimited database resources.
- D. They are free of charge for all users.
正解：A
解説：
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.
質問 # 15
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Weigh the importance of different words within a sequence and understand the context.
- B. Apply a specific function to each word individually.
- C. Convert tokens into numerical forms (vectors) that the model can understand.
- D. Break down a sentence into smaller pieces called tokens.
正解：A
解説：
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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質問 # 16
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Prevention of harm
- B. Fairness
- C. Respect for human autonomy
- D. Explicability
正解：D
質問 # 17
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Embedding models
- B. Chat models
- C. Generation models
- D. Translation models
正解：D
解説：
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.
質問 # 18
In machine learning, what does the term "model training" mean?
- A. Performing data analysis on collected and labeled data
- B. Writing code for the entire program
- C. Establishing a relationship between input features and output
- D. Analyzing the accuracy of a trained model
正解：C
解説：
In machine learning, "model training" refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
質問 # 19
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
- A. Anomaly Detection
- B. Natural Language Processing
- C. Natural Language Processing
- D. Computer Vision
正解：B
解説：
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.
質問 # 20
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Analyzing historical data for unusual patterns
- B. Detecting vehicle number plates to issue speed citations
- C. Detecting and preventing fraud in financial transactions
- D. Generating realistic images from text
正解：B
解説：
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.
質問 # 21
How does AI enhance human efforts?
- A. By processing data at a speed and effectiveness far beyond human capability
- B. By deleting data humans need to handle
- C. By increasing the physical strength of humans
- D. By completely replacing human workers in all tasks
正解：A
解説：
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI's ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.
質問 # 22
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Audio recording
- B. Speech recognition
- C. Text to speech
- D. Text summarization
正解：C
解説：
For a multilingual public announcement system, the AI task that would be most relevant is "Text to Speech" (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
質問 # 23
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Active learning
- B. Reinforcement learning
- C. Unsupervised learning
- D. Supervised learning
正解：C
解説：
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .
質問 # 24
What is "in-context learning" in the realm of Large Language Models (LLMs)?
- A. Training a model on a diverse range of tasks
- B. Modifying the behavior of a pretrained LLM permanently
- C. Providing a few examples of a target task via the input prompt
- D. Teaching a model through zero-shot learning
正解：C
解説：
"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt. This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.
質問 # 25
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. Binary Classification
- B. Multi-Class Classification
- C. Regression
- D. Clustering
正解：B
解説：
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
質問 # 26
What is the primary benefit of using the OCI Language service for text analysis?
- A. It allows for text analysis at scale without machine learning expertise.
- B. It requires extensive machine learning expertise to use.
- C. It only works with structured data.
- D. It provides image processing capabilities.
正解：A
解説：
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.
質問 # 27
Which feature is NOT available as part of OCI Speech capabilities?
- A. Provides timestamped, grammatically accurate transcriptions
- B. Supports multiple languages including English, Spanish, and Portuguese
- C. Transcribes audio and video files into text
- D. Uses extensive data science experience to operate
正解：D
解説：
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs. These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.
質問 # 28
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Converting text into images
- B. Detecting objects and scenes in images
- C. Translating text into speech
- D. Analyzing text to extract structured information like sentiment or entities
正解：D
解説：
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.
質問 # 29
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