[更新されたのは2025年]Oracle 1z0-1122-24問題準備には無料サンプルのPDF
2025年最新の認定サンプル問題1z0-1122-24問題集と練習試験合格させます
Oracle 1z0-1122-24 認定試験の出題範囲：
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質問 # 13
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Notebook sessions
- B. Conda environment
- C. Accelerated Data Science (ADS) SDK
- D. Model catalog
正解：A
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 # 14
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Sentiment Analysis
- B. Text Classification
- C. Text Generation
- D. Language Detection
正解：C
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 # 15
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It recognizes and extracts text from a document.
- B. It provides real-time translation of text.
- C. It converts audio files into text.
- D. It enhances the visual quality of documents.
正解：A
解説：
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI's OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.
質問 # 16
What is the primary purpose of reinforcement learning?
- A. Finding relationships within data sets
- B. Making predictions from labeled data
- C. Identifying patterns in data
- D. Learning from outcomes to make decisions
正解：D
解説：
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.
質問 # 17
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Chat models
- B. Embedding models
- C. Translation models
- D. Generation models
正解：C
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
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. DL is a subset of AI, and ML is a subset of DL.
- B. ML is a subset of AI, and DL is a subset of ML.
- C. AI is a subset of DL, which is a subset of ML.
- D. AI, ML, and DL are entirely separate fields with no overlap.
正解：B
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 # 19
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Detecting and preventing fraud in financial transactions
- B. Analyzing historical data for unusual patterns
- C. Detecting vehicle number plates to issue speed citations
- D. Generating realistic images from text
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 # 20
Which feature is NOT available as part of OCI Speech capabilities?
- A. Supports multiple languages including English, Spanish, and Portuguese
- B. Uses extensive data science experience to operate
- C. Provides timestamped, grammatically accurate transcriptions
- D. Transcribes audio and video files into text
正解：B
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 # 21
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Extract tables from documents
- B. Generate transcript from documents
- C. Classify documents into different types
- D. Extract text 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 # 22
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
- A. Backpropagation
- B. Random Forest
- C. Gradient Descent
- D. Support Vector Machine
正解：A
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 # 23
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 # 24
What is the key feature of Recurrent Neural Networks (RNNs)?
- A. They are primarily used for image recognition tasks.
- B. They process data in parallel.
- C. They do not have an internal state.
- D. They have a feedback loop that allows information to persist across different time steps.
正解：D
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 # 25
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Chat models
- B. Embedding models
- C. Translation models
- D. Generation models
正解：C
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 # 26
What is the primary benefit of using the OCI Language service for text analysis?
- A. It only works with structured data.
- B. It provides image processing capabilities.
- C. It allows for text analysis at scale without machine learning expertise.
- D. It requires extensive machine learning expertise to use.
正解：C
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
What is the benefit of using embedding models in OCI Generative AI service?
- A. They optimize the use of computational resources.
- B. They enable creating detailed graphics.
- C. They simplify managing databases.
- D. They facilitate semantic searches.
正解：D
解説：
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for "financial reports," an embedding model can understand that "quarterly earnings" is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .
質問 # 28
What distinguishes Generative AI from other types of AI?
- A. Generative AI uses algorithms to predict outcomes based on past data.
- B. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- C. Generative AI focuses on making decisions based on user interactions.
- D. Generative AI involves training models to perform tasks without human intervention.
正解：B
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 # 29
What are Convolutional Neural Networks (CNNs) primarily used for?
- A. Text processing
- B. Time series prediction
- C. Image generation
- D. Image classification
正解：D
解説：
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.
質問 # 30
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