[2025年05月01日]1z0-1122-24試験問題集、1z0-1122-24練習テスト問題
無料で使える1z0-1122-24学習ガイド試験問題と解答
Oracle 1z0-1122-24 認定試験の出題範囲：
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質問 # 20
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Weigh the importance of different words within a sequence and understand the context.
- B. Break down a sentence into smaller pieces called tokens.
- C. Apply a specific function to each word individually.
- D. Convert tokens into numerical forms (vectors) that the model can understand.
正解：A
解説：
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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質問 # 21
What would you use Oracle AI Vector Search for?
- A. Query data based on semantics.
- B. Query data based on keywords.
- C. Manage database security protocols.
- D. Store business data in a cloud database.
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 # 22
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 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 # 23
What is the purpose of the model catalog in OCI Data Science?
- A. To provide a preinstalled open source library
- B. To deploy models as HTTP endpoints
- C. To store, track, share, and manage models
- D. To create and switch between different environments
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
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Extract tables from documents
- B. Classify documents into different types
- C. Generate transcript from documents
- D. Extract text from documents
正解：C
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 # 25
What is the key feature of Recurrent Neural Networks (RNNs)?
- A. They do not have an internal state.
- B. They are primarily used for image recognition tasks.
- C. They have a feedback loop that allows information to persist across different time steps.
- D. They process data in parallel.
正解：C
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 # 26
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. Reinforcement learning
- C. Unsupervised learning
- D. Supervised 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 # 27
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Directly predicting the final output
- B. Providing labels for the output neurons
- C. Storing the input pixel values
- 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 # 28
How does AI enhance human efforts?
- A. By completely replacing human workers in all tasks
- B. By increasing the physical strength of humans
- C. By deleting data humans need to handle
- D. By processing data at a speed and effectiveness far beyond human capability
正解：D
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 # 29
Which feature is NOT available as part of OCI Speech capabilities?
- A. Supports multiple languages including English, Spanish, and Portuguese
- B. Transcribes audio and video files into text
- C. Uses extensive data science experience to operate
- D. Provides timestamped, grammatically accurate transcriptions
正解：C
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 # 30
Which AI domain can be employed for identifying patterns in images and extract relevant features?
- A. Natural Language Processing
- B. Anomaly Detection
- C. Speech Processing
- 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 # 31
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Text to speech
- B. Text summarization
- C. Speech recognition
- D. Audio recording
正解：A
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 # 32
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Sentiment Analysis
- B. Text Generation
- C. Text Classification
- 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 # 33
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. Language
- B. Speech
- C. Document Understanding
- D. Vision
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 # 34
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. AI is a subset of DL, which is a subset of ML.
- B. ML is a subset of AI, and DL is a subset of ML.
- C. AI, ML, and DL are entirely separate fields with no overlap.
- D. DL is a subset of AI, and ML is a subset of DL.
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 # 35
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 prioritize larger model sizes to achieve better performance.
- C. They focus on increasing the number of tokens while keeping the model size constant.
- D. They disregard model size and prioritize high-quality data only.
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 # 36
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Prevention of harm
- B. Respect for human autonomy
- C. Explicability
- D. Fairness
正解：C
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 # 37
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1z0-1122-24試験問題集、1z0-1122-24練習テスト問題：https://www.passtest.jp/Oracle/1z0-1122-24-shiken.html
検証済み1z0-1122-24問題集PDF資料 [2025年更新]：https://drive.google.com/open?id=1nGf_sXpEGlBPhfZTQRqBo3joDY3-jWif