[2025年03月]更新の1z0-1122-24試験問題集、無料サンプル365日更新
まもなく無料セール終了！リアル1z0-1122-24のPDF解答使おう
質問 # 25
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Notebook sessions
- C. Accelerated Data Science (ADS) SDK
- D. Model catalog
正解：B
解説：
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
質問 # 26
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 and regression both predict continuous values.
- D. Classification assigns data points to categories, whereas regression predicts continuous values.
正解：D
解説：
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".
質問 # 27
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Analyzing historical data for unusual patterns
- B. Generating realistic images from text
- C. Detecting vehicle number plates to issue speed citations
- D. Detecting and preventing fraud in financial transactions
正解：C
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 # 28
What is "in-context learning" in the realm of Large Language Models (LLMs)?
- A. Providing a few examples of a target task via the input prompt
- B. Modifying the behavior of a pretrained LLM permanently
- C. Training a model on a diverse range of tasks
- D. Teaching a model through zero-shot learning
正解：A
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 # 29
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Classify documents into different types
- B. Generate transcript from documents
- C. Extract text from documents
- D. Extract tables from documents
正解：B
解説：
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
質問 # 30
In machine learning, what does the term "model training" mean?
- A. Analyzing the accuracy of a trained model
- B. Writing code for the entire program
- C. Establishing a relationship between input features and output
- D. Performing data analysis on collected and labeled data
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 # 31
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
- A. Document Understanding
- B. Speech
- C. Vision
- D. Language
正解：B
解説：
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the "Speech" service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.
質問 # 32
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Supervised learning
- B. Active 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 # 33
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 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 # 34
How does AI enhance human efforts?
- A. By increasing the physical strength of humans
- B. By completely replacing human workers in all tasks
- C. By processing data at a speed and effectiveness far beyond human capability
- D. By deleting data humans need to handle
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 # 35
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Storing the input pixel values
- B. Providing labels for the output neurons
- C. Directly predicting the final output
- D. Capturing the internal representation of the raw image data
正解：D
解説：
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
質問 # 36
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It is ideal for tasks such as text-to-speech conversion.
- B. It offers seamless integration with social media platforms.
- C. It delivers exceptional performance and scalability for complex AI tasks.
- D. It provides a cost-effective solution for simple AI tasks.
正解：C
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 # 37
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. Regression
- B. Clustering
- 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 # 38
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Adjusting the model parameters to improve accuracy
- B. Encrypting the data for security reasons
- C. Doubling the neural network layers
- D. Upgrading the hardware of the AI clusters
正解：A
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 # 39
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Text Classification
- B. Text Generation
- C. Sentiment Analysis
- D. Language Detection
正解：B
解説：
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.
質問 # 40
What distinguishes Generative AI from other types of AI?
- A. Generative AI uses algorithms to predict outcomes based on past data.
- B. Generative AI focuses on making decisions based on user interactions.
- C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- D. Generative AI involves training models to perform tasks without human intervention.
正解：C
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 # 41
What is the key feature of Recurrent Neural Networks (RNNs)?
- A. They process data in parallel.
- B. They have a feedback loop that allows information to persist across different time steps.
- C. They are primarily used for image recognition tasks.
- D. They do not have an internal state.
正解：B
解説：
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by "remembering" past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.
質問 # 42
......
1z0-1122-24問題集あなたを合格させる認証試験：https://www.passtest.jp/Oracle/1z0-1122-24-shiken.html
最新でリアルなOracle 1z0-1122-24試験問題集解答：https://drive.google.com/open?id=1nGf_sXpEGlBPhfZTQRqBo3joDY3-jWif