1Z0-1122-25問題集PDFで1Z0-1122-25リアル試験問題解答
時間限定！今すぐ試そう1Z0-1122-25試験 [2025] 問題集でOracleのPDF問題
Oracle 1Z0-1122-25 認定試験の出題範囲：
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質問 # 23
What would you use Oracle AI Vector Search for?
- A. Query data based on semantics.
- B. Query data based on keywords.
- C. Store business data in a cloud database.
- D. Manage database security protocols.
正解：A
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 # 24
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 # 25
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Embedding models
- B. Chat 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
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 # 27
What role do Transformers perform in Large Language Models (LLMs)?
- A. Manually engineer features in the data before training the model
- B. Image recognition tasks in LLMs
- C. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
- D. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
正解：D
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 # 28
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
- A. By automating data extraction from documents
- B. By generating lifelike speech from documents
- C. By transcribing spoken language
- D. By analyzing sentiment in text documents
正解：A
解説：
Explanation:
質問 # 29
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They ensure that the model size, training time, and data size are balanced for optimal results.
- B. They disregard model size and prioritize high-quality data only.
- C. They prioritize larger model sizes to achieve better performance.
- D. They focus on increasing the number of tokens while keeping the model size constant.
正解：A
解説：
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.
質問 # 30
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. 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 # 31
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Explicability
- B. Respect for human autonomy
- C. Prevention of harm
- D. Fairness
正解：A
解説：
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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質問 # 32
What would you use Oracle AI Vector Search for?
- A. Query data based on semantics.
- B. Query data based on keywords.
- C. Store business data in a cloud database.
- D. Manage database security protocols.
正解：A
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 # 33
How does AI enhance human efforts?
- A. By processing data at a speed and effectiveness far beyond human capability
- B. By completely replacing human workers in all tasks
- C. By increasing the physical strength of humans
- D. By deleting data humans need to handle
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 # 34
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 # 35
What is the purpose of the model catalog in OCI Data Science?
- A. To create and switch between different environments
- B. To store, track, share, and manage models
- C. To deploy models as HTTP endpoints
- D. To provide a preinstalled open source library
正解：B
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 # 36
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Text normalization
- B. Timestamping
- C. Profanity filtering
- D. Audio tuning
正解：A
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 # 37
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Speech recognition
- B. Text to speech
- C. Audio recording
- D. Text summarization
正解：B
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 # 38
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
- B. Both involve retraining the model, but Prompt Engineering does it more often.
- C. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
- D. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
正解：A
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 # 39
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：C
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 # 40
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