2024年最新の実際に出る無料Oracle 1z0-1122-24試験問題集と解答
1z0-1122-24練習テストエンジンで今すぐ試そう43試験問題
質問 # 22
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It enhances the visual quality of documents.
- B. It recognizes and extracts text from a document.
- C. It converts audio files into text.
- D. It provides real-time translation of text.
正解：B
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 # 23
What is the primary benefit of using the OCI Language service for text analysis?
- A. It requires extensive machine learning expertise to use.
- B. It only works with structured data.
- C. It allows for text analysis at scale without machine learning expertise.
- D. It provides image processing capabilities.
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 # 24
What distinguishes Generative AI from other types of AI?
- A. Generative AI involves training models to perform tasks without human intervention.
- 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 uses algorithms to predict outcomes based on past data.
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 # 25
What would you use Oracle AI Vector Search for?
- A. Query data based on keywords.
- B. Store business data in a cloud database.
- C. Query data based on semantics.
- D. Manage database security protocols.
正解：C
解説：
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
質問 # 26
How does AI enhance human efforts?
- A. By processing data at a speed and effectiveness far beyond human capability
- B. By increasing the physical strength of humans
- C. By deleting data humans need to handle
- 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 # 27
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Generating realistic images from text
- B. Analyzing historical data for unusual patterns
- C. Detecting and preventing fraud in financial transactions
- 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 # 28
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Text Classification
- B. Sentiment Analysis
- C. Language Detection
- D. Text Generation
正解：D
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 # 29
What role do Transformers perform in Large Language Models (LLMs)?
- A. Image recognition tasks in LLMs
- B. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
- C. Manually engineer features in the data before training the model
- D. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
正解：B
解説：
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.
This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.
質問 # 30
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Generate transcript from documents
- B. Extract text from documents
- C. Classify documents into different types
- D. Extract tables from documents
正解：A
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 # 31
Which AI domain can be employed for identifying patterns in images and extract relevant features?
- A. Natural Language Processing
- B. Speech Processing
- C. Anomaly Detection
- D. Computer Vision
正解：D
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 # 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. Reinforcement learning
- C. Unsupervised learning
- D. Active 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 # 33
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Detecting objects and scenes in images
- B. Analyzing text to extract structured information like sentiment or entities
- C. Translating text into speech
- D. Converting text into images
正解：B
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 # 34
What is the difference between classification and regression in Supervised Machine Learning?
- A. Classification predicts continuous values, whereas regression assigns data points to categories.
- B. Classification and regression both assign 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：
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 # 35
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 creates input prompts, while Fine-tuning retrains the model on specific data.
- C. Both involve retraining the model, but Prompt Engineering does it more often.
- D. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
正解：B
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 # 36
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. Capturing the internal representation of the raw image data
- C. Providing labels for the output neurons
- D. Directly predicting the final output
正解：B
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 # 37
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It delivers exceptional performance and scalability for complex AI tasks.
- B. It offers seamless integration with social media platforms.
- C. It is ideal for tasks such as text-to-speech conversion.
- D. It provides a cost-effective solution for simple AI tasks.
正解：A
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 # 38
What is the primary purpose of reinforcement learning?
- A. Making predictions from labeled data
- B. Learning from outcomes to make decisions
- C. Finding relationships within data sets
- D. Identifying patterns in data
正解：B
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 # 39
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Oracle 1z0-1122-24日常練習試験は2024年最新のに更新された43問あります：https://drive.google.com/open?id=1boa7kpj6Wtp0Drf7TqthtW_HjsWPdZzA