[2025年更新]早速ゲットしてトップランクのOracle 1z0-1122-24試験問題集
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Oracle 1z0-1122-24 認定試験の出題範囲：
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質問 # 11
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Text normalization
- B. Profanity filtering
- C. Audio tuning
- D. Timestamping
正解：A
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 # 12
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Break down a sentence into smaller pieces called tokens.
- B. Convert tokens into numerical forms (vectors) that the model can understand.
- C. Apply a specific function to each word individually.
- D. Weigh the importance of different words within a sequence and understand the context.
正解：D
解説：
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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質問 # 13
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
- A. They are free of charge for all users.
- B. They allow access to unlimited database resources.
- C. They provide faster internet connection speeds.
- D. They provide high performance compute resources for fine-tuning tasks.
正解：D
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 # 14
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. Gradient Descent
- C. Backpropagation
- D. Random Forest
正解：C
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 # 15
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
- B. Both involve retraining the model, but Prompt Engineering does it more often.
- 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 # 16
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Converting text into images
- B. Translating text into speech
- C. Analyzing text to extract structured information like sentiment or entities
- D. Detecting objects and scenes in images
正解：C
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
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Speech recognition
- B. Text to speech
- C. Audio recording
- D. Text summarization
正解：B
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 # 18
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?
- A. Language Detection
- B. Sentiment Analysis
- C. Text Classification
- 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 # 19
What distinguishes Generative AI from other types of AI?
- A. Generative AI focuses on making decisions based on user interactions.
- B. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- C. Generative AI uses algorithms to predict outcomes based on past data.
- D. Generative AI involves training models to perform tasks without human intervention.
正解：B
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 # 20
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Doubling the neural network layers
- B. Adjusting the model parameters to improve accuracy
- C. Upgrading the hardware of the AI clusters
- D. Encrypting the data for security reasons
正解：B
解説：
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.
質問 # 21
Which feature is NOT available as part of OCI Speech capabilities?
- A. Supports multiple languages including English, Spanish, and Portuguese
- B. Uses extensive data science experience to operate
- C. Provides timestamped, grammatically accurate transcriptions
- 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 # 22
What is the benefit of using embedding models in OCI Generative AI service?
- A. They optimize the use of computational resources.
- B. They facilitate semantic searches.
- C. They simplify managing databases.
- D. They enable creating detailed graphics.
正解：B
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 # 23
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Extract text from documents
- B. Classify documents into different types
- C. Extract tables from documents
- D. Generate transcript from documents
正解：D
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 # 24
What is the primary purpose of reinforcement learning?
- A. Making predictions from labeled data
- B. Finding relationships within data sets
- C. Learning from outcomes to make decisions
- D. Identifying patterns in data
正解：C
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 # 25
In machine learning, what does the term "model training" mean?
- A. Analyzing the accuracy of a trained model
- B. Establishing a relationship between input features and output
- C. Writing code for the entire program
- D. Performing data analysis on collected and labeled data
正解：B
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 # 26
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Notebook sessions
- C. Model catalog
- 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 # 27
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 # 28
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