[2025年02月]更新の1z0-1122-24試験事前練習テスト試験問題と解答Oracle Cloud学習ガイド
Oracle Cloud Infrastructure 2024 AI Foundations Associate認証サンプル解答
Oracle 1z0-1122-24 認定試験の出題範囲：
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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. Uses extensive data science experience to operate
- D. Transcribes audio and video files into text
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 # 25
What are Convolutional Neural Networks (CNNs) primarily used for?
- A. Image classification
- B. Image generation
- C. Time series prediction
- D. Text processing
正解：A
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
What is "in-context learning" in the realm of Large Language Models (LLMs)?
- A. Modifying the behavior of a pretrained LLM permanently
- B. Providing a few examples of a target task via the input prompt
- C. Training a model on a diverse range of tasks
- D. Teaching a model through zero-shot learning
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 # 27
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 # 28
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Model catalog
- B. Notebook sessions
- C. Conda environment
- D. Accelerated Data Science (ADS) SDK
正解：B
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 # 29
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Speech recognition
- B. Audio recording
- C. Text summarization
- 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 # 30
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
質問 # 31
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Storing the input pixel values
- B. Providing labels for the output neurons
- C. Capturing the internal representation of the raw image data
- D. Directly predicting the final output
正解：C
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 # 32
What is the primary benefit of using the OCI Language service for text analysis?
- A. It provides image processing capabilities.
- B. It requires extensive machine learning expertise to use.
- C. It allows for text analysis at scale without machine learning expertise.
- D. It only works with structured data.
正解：C
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 # 33
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 # 34
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Embedding models
- B. Generation models
- C. Chat models
- D. Translation models
正解：D
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 # 35
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They focus on increasing the number of tokens while keeping the model size constant.
- B. They ensure that the model size, training time, and data size are balanced for optimal results.
- C. They disregard model size and prioritize high-quality data only.
- D. They prioritize larger model sizes to achieve better performance.
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 # 36
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 # 37
What distinguishes Generative AI from other types of AI?
- A. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- B. Generative AI involves training models to perform tasks without human intervention.
- C. Generative AI focuses on making decisions based on user interactions.
- D. Generative AI uses algorithms to predict outcomes based on past data.
正解：A
解説：
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.
質問 # 38
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. Backpropagation
- C. Support Vector Machine
- D. Gradient Descent
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 # 39
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Reinforcement learning
- B. Active learning
- C. Supervised learning
- D. Unsupervised learning
正解：D
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 # 40
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