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合格Oracle Cloud 1z0-1122-24試験問題には43問があります
Oracle 1z0-1122-24 認定試験の出題範囲：
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質問 # 12
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. Supervised learning
- C. Reinforcement learning
- D. Unsupervised learning
正解：D
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 # 13
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Doubling the neural network layers
- B. Upgrading the hardware of the AI clusters
- C. Encrypting the data for security reasons
- D. Adjusting the model parameters to improve accuracy
正解：D
解説：
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
質問 # 14
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. ML is a subset of AI, and DL is a subset of ML.
- B. AI, ML, and DL are entirely separate fields with no overlap.
- C. DL is a subset of AI, and ML is a subset of DL.
- D. AI is a subset of DL, which is a subset of ML.
正解：A
解説：
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence "deep").
In this hierarchy:
AI includes all methods to make machines intelligent.
ML refers to the methods within AI that focus on learning from data.
DL is a specialized field within ML that deals with deep neural networks.
質問 # 15
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Detecting objects and scenes in images
- B. Translating text into speech
- C. Analyzing text to extract structured information like sentiment or entities
- D. Converting text into images
正解：C
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 # 16
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Analyzing historical data for unusual patterns
- B. Detecting and preventing fraud in financial transactions
- C. Generating realistic images from text
- D. Detecting vehicle number plates to issue speed citations
正解：D
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 # 17
Which AI domain can be employed for identifying patterns in images and extract relevant features?
- A. Anomaly Detection
- B. Computer Vision
- C. Speech Processing
- D. Natural Language Processing
正解：B
解説：
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.
質問 # 18
What is the primary benefit 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 designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.
質問 # 19
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
- A. By automating data extraction from documents
- B. By analyzing sentiment in text documents
- C. By transcribing spoken language
- D. By generating lifelike speech from documents
正解：A
解説：
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.
質問 # 20
How does AI enhance human efforts?
- A. By increasing the physical strength of humans
- B. By deleting data humans need to handle
- C. By processing data at a speed and effectiveness far beyond human capability
- D. By completely replacing human workers in all tasks
正解：C
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 # 21
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. Gradient Descent
- C. Support Vector Machine
- D. Backpropagation
正解：D
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 # 22
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Respect for human autonomy
- B. Fairness
- C. Prevention of harm
- D. Explicability
正解：D
質問 # 23
What is the purpose of the model catalog in OCI Data Science?
- A. To deploy models as HTTP endpoints
- B. To create and switch between different environments
- C. To store, track, share, and manage models
- D. To provide a preinstalled open source library
正解：C
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 # 24
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
- B. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
- C. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
- D. Both involve retraining the model, but Prompt Engineering does it more often.
正解：C
解説：
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model's internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
Fine-tuning, on the other hand, refers to the process of retraining a pretrained model on a smaller, task-specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively "specializing" the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.
質問 # 25
What key objective does machine learning strive to achieve?
- A. Enabling computers to learn and improve from experience
- B. Creating algorithms to solve complex problems
- C. Explicitly programming computers
- D. Improving computer hardware
正解：A
解説：
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.
質問 # 26
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. Regression
- C. Multi-Class Classification
- D. Binary Classification
正解：C
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 # 27
What distinguishes Generative AI from other types of AI?
- A. Generative AI involves training models to perform tasks without human intervention.
- B. Generative AI uses algorithms to predict outcomes based on past data.
- C. Generative AI focuses on making decisions based on user interactions.
- D. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
正解：D
解説：
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.
質問 # 28
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Profanity filtering
- B. Audio tuning
- C. Timestamping
- D. Text normalization
正解：D
解説：
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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質問 # 29
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