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Oracle 1z0-1122-24 認定試験の出題範囲：
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質問 # 18
What is the difference between classification and regression in Supervised Machine Learning?
- A. Classification and regression both predict continuous values.
- B. Classification assigns data points to categories, whereas regression predicts continuous values.
- C. Classification and regression both assign data points to categories.
- D. Classification predicts continuous values, whereas regression assigns data points to categories.
正解：B
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 # 19
Which AI domain can be employed for identifying patterns in images and extract relevant features?
- A. Computer Vision
- B. Natural Language Processing
- C. Speech Processing
- D. Anomaly Detection
正解：A
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 # 20
What is the purpose of the model catalog in OCI Data Science?
- A. To deploy models as HTTP endpoints
- B. To store, track, share, and manage models
- C. To provide a preinstalled open source library
- D. To create and switch between different environments
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 # 21
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Audio tuning
- B. Timestamping
- C. Text normalization
- D. Profanity filtering
正解：C
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 # 22
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Extract text from documents
- B. Generate transcript from documents
- C. Classify documents into different types
- 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 # 23
Which feature is NOT available as part of OCI Speech capabilities?
- A. Provides timestamped, grammatically accurate transcriptions
- B. Uses extensive data science experience to operate
- C. Supports multiple languages including English, Spanish, and Portuguese
- 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 # 24
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. Converting text into images
- D. Translating text into speech
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 # 25
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Audio recording
- B. Text summarization
- C. Speech recognition
- D. Text to speech
正解：D
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 # 26
What role do Transformers perform in Large Language Models (LLMs)?
- A. Image recognition tasks in LLMs
- B. Manually engineer features in the data before training the model
- C. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
- D. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
正解：C
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 # 27
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. Vision
- B. Language
- C. Document Understanding
- D. Speech
正解：D
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 # 28
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It provides real-time translation of text.
- B. It enhances the visual quality of documents.
- C. It converts audio files into text.
- D. It recognizes and extracts text from a document.
正解：D
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 # 29
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 # 30
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. Teaching a model through zero-shot learning
- C. Modifying the behavior of a pretrained LLM permanently
- D. Training a model on a diverse range of tasks
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 # 31
How does AI enhance human efforts?
- A. By increasing the physical strength of humans
- B. By deleting data humans need to handle
- C. By processing data at a speed and effectiveness far beyond human capability
- D. By completely replacing human workers in all tasks
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 # 32
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 # 33
What is the primary purpose of reinforcement learning?
- A. Finding relationships within data sets
- B. Learning from outcomes to make decisions
- C. Making predictions from labeled data
- 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 # 34
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Prevention of harm
- B. Explicability
- C. Respect for human autonomy
- D. Fairness
正解：B
質問 # 35
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