最新の1z0-1122-24学習ガイド2024年最新の- 提供するのはテストエンジンとPDF
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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 primary benefit of using the OCI Language service for text analysis?
- A. It allows for text analysis at scale without machine learning expertise.
- B. It only works with structured data.
- C. It requires extensive machine learning expertise to use.
- D. It provides image processing capabilities.
正解：A
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 # 19
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
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 # 20
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 # 21
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Both involve retraining the model, but Prompt Engineering does it more often.
- B. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
- C. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
- D. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
正解：C
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 # 22
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 # 23
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 # 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
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 # 26
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 # 27
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
- A. Gradient Descent
- B. Backpropagation
- C. Random Forest
- D. Support Vector Machine
正解：B
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 # 28
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Language Detection
- B. Text Classification
- C. Sentiment Analysis
- 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
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 # 30
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 # 31
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 provides a cost-effective solution for simple AI tasks.
- C. It offers seamless integration with social media platforms.
- D. It is ideal for tasks such as text-to-speech conversion.
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 # 32
What key objective does machine learning strive to achieve?
- A. Improving computer hardware
- B. Creating algorithms to solve complex problems
- C. Enabling computers to learn and improve from experience
- D. Explicitly programming computers
正解：C
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 # 33
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 # 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
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