[2025年04月18日] 完全版には更新されたのはOracle Cloud（1z0-1122-24）認定サンプル問題
最新のOracle 1z0-1122-24リアル試験問題集PDF
質問 # 10
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. Document Understanding
- B. Speech
- C. Language
- 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 # 11
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 # 12
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. Multi-Class Classification
- B. Clustering
- C. Regression
- D. Binary Classification
正解：A
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 # 13
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Speech recognition
- B. Text summarization
- C. Audio recording
- 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 # 14
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Detecting vehicle number plates to issue speed citations
- B. Analyzing historical data for unusual patterns
- C. Detecting and preventing fraud in financial transactions
- D. Generating realistic images from text
正解：A
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 # 15
What is the difference between classification and regression in Supervised Machine Learning?
- A. Classification and regression both predict continuous values.
- B. Classification predicts continuous values, whereas regression assigns data points to categories.
- C. Classification and regression both assign data points to categories.
- D. Classification assigns data points to categories, whereas regression predicts continuous values.
正解：D
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 # 16
What would you use Oracle AI Vector Search for?
- A. Query data based on semantics.
- B. Manage database security protocols.
- C. Query data based on keywords.
- 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 # 17
What is the benefit of using embedding models in OCI Generative AI service?
- A. They simplify managing databases.
- B. They enable creating detailed graphics.
- C. They optimize the use of computational resources.
- 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 # 18
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They disregard model size and prioritize high-quality data only.
- B. They ensure that the model size, training time, and data size are balanced for optimal results.
- 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：B
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 # 19
What is "in-context learning" in the realm of Large Language Models (LLMs)?
- A. Teaching a model through zero-shot learning
- B. Providing a few examples of a target task via the input prompt
- C. Training a model on a diverse range of tasks
- D. Modifying the behavior of a pretrained LLM permanently
正解：B
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 # 20
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Accelerated Data Science (ADS) SDK
- C. Notebook sessions
- D. Model catalog
正解：C
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 # 21
In machine learning, what does the term "model training" mean?
- A. Writing code for the entire program
- B. Performing data analysis on collected and labeled data
- C. Analyzing the accuracy of a trained model
- D. Establishing a relationship between input features and output
正解：D
解説：
In machine learning, "model training" refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
質問 # 22
What key objective does machine learning strive to achieve?
- A. Creating algorithms to solve complex problems
- B. Improving computer hardware
- C. Explicitly programming computers
- D. Enabling computers to learn and improve from experience
正解：D
解説：
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.
質問 # 23
What is the key feature of Recurrent Neural Networks (RNNs)?
- A. They process data in parallel.
- B. They have a feedback loop that allows information to persist across different time steps.
- C. They are primarily used for image recognition tasks.
- D. They do not have an internal state.
正解：B
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 # 24
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
- A. Random Forest
- B. Gradient Descent
- C. Support Vector Machine
- D. Backpropagation
正解：D
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 # 25
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 # 26
Which feature is NOT available as part of OCI Speech capabilities?
- A. Uses extensive data science experience to operate
- B. Transcribes audio and video files into text
- C. Supports multiple languages including English, Spanish, and Portuguese
- D. Provides timestamped, grammatically accurate transcriptions
正解：A
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 # 27
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