更新されたPDF（2024年最新）実際にあるOracle 1z0-1122-24試験問題
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質問 # 15
What would you use Oracle AI Vector Search for?
- A. Store business data in a cloud database.
- B. Manage database security protocols.
- C. Query data based on semantics.
- D. Query data based on keywords.
正解：C
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 # 16
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Converting text into images
- B. Analyzing text to extract structured information like sentiment or entities
- C. Detecting objects and scenes in 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 # 17
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?
- A. Support Vector Machine
- B. Random Forest
- C. Gradient Descent
- 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 # 18
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Accelerated Data Science (ADS) SDK
- B. Notebook sessions
- C. Model catalog
- D. Conda environment
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 # 19
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
- A. They provide high performance compute resources for fine-tuning tasks.
- B. They allow access to unlimited database resources.
- C. They provide faster internet connection speeds.
- D. They are free of charge for all users.
正解：A
解説：
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.
質問 # 20
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. Speech
- D. Document Understanding
正解：C
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 # 21
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Translation models
- B. Embedding models
- C. Chat models
- D. Generation models
正解：A
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 # 22
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Capturing the internal representation of the raw image data
- B. Directly predicting the final output
- C. Providing labels for the output neurons
- D. Storing the input pixel values
正解：A
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 # 23
What are Convolutional Neural Networks (CNNs) primarily used for?
- A. Time series prediction
- B. Text processing
- C. Image classification
- D. Image generation
正解：C
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 # 24
What role do Transformers perform in Large Language Models (LLMs)?
- A. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
- B. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
- C. Manually engineer features in the data before training the model
- D. Image recognition tasks in LLMs
正解：B
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 # 25
What key objective does machine learning strive to achieve?
- A. Explicitly programming computers
- B. Enabling computers to learn and improve from experience
- C. Improving computer hardware
- D. Creating algorithms to solve complex problems
正解：B
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 # 26
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It converts audio files into text.
- B. It provides real-time translation of text.
- C. It enhances the visual quality of documents.
- 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 # 27
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 disregard model size and prioritize high-quality data only.
- D. They focus on increasing the number of tokens while keeping the model size constant.
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 # 28
What is the difference between classification and regression in Supervised Machine Learning?
- A. Classification and regression both assign data points to categories.
- B. Classification predicts continuous values, whereas regression assigns data points to categories.
- C. Classification and regression both predict continuous values.
- 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 # 29
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Prevention of harm
- B. Fairness
- C. Respect for human autonomy
- D. Explicability
正解：D
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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