1Z0-1122-25認定お試し[2025年12月04日] 最新1Z0-1122-25のPDF問題集
ベストOracle 1Z0-1122-25学習ガイドと問題集でof2025年更新
Oracle 1Z0-1122-25 認定試験の出題範囲：
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
| トピック 5 |
|
| トピック 6 |
|
質問 # 22
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 focus on increasing the number of tokens while keeping the model size constant.
- C. They disregard model size and prioritize high-quality data only.
- D. They prioritize larger model sizes to achieve better performance.
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 # 23
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?
- A. DL is a subset of AI, and ML is a subset of DL.
- B. ML is a subset of AI, and DL is a subset of ML.
- C. AI is a subset of DL, which is a subset of ML.
- D. AI, ML, and DL are entirely separate fields with no overlap.
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 # 24
Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Audio tuning
- B. Timestamping
- C. Profanity filtering
- D. Text normalization
正解：D
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.
Top of Form
Bottom of Form
質問 # 25
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. Document Understanding
- C. Language
- D. Speech
正解：D
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 # 26
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Fairness
- B. Respect for human autonomy
- C. Explicability
- D. Prevention of harm
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.
Top of Form
Bottom of Form
質問 # 27
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 # 28
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
- A. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
- B. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
- C. Both involve retraining the model, but Prompt Engineering does it more often.
- D. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
正解：A
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 # 29
What does "fine-tuning" refer to in the context of OCI Generative AI service?
- A. Encrypting the data for security reasons
- B. Adjusting the model parameters to improve accuracy
- C. Doubling the neural network layers
- D. Upgrading the hardware of the AI clusters
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 # 30
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 # 31
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Active learning
- B. Unsupervised learning
- C. Supervised learning
- D. Reinforcement learning
正解：B
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 # 32
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
- A. It converts audio files into text.
- B. It enhances the visual quality of documents.
- C. It recognizes and extracts text from a document.
- D. It provides real-time translation of text.
正解：C
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 # 33
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 # 34
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. Backpropagation
- D. Gradient Descent
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 # 35
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Audio recording
- B. Speech recognition
- C. Text to speech
- D. Text summarization
正解：C
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 # 36
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 offers seamless integration with social media platforms.
- C. It is ideal for tasks such as text-to-speech conversion.
- D. It provides a cost-effective solution for simple AI tasks.
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 # 37
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 provide high performance compute resources for fine-tuning tasks.
- C. They provide faster internet connection speeds.
- D. They allow access to unlimited database resources.
正解：B
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 # 38
What distinguishes Generative AI from other types of AI?
- A. Generative AI uses algorithms to predict outcomes based on past data.
- B. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
- C. Generative AI involves training models to perform tasks without human intervention.
- D. Generative AI focuses on making decisions based on user interactions.
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 # 39
......
有効な1Z0-1122-25試験 最新問題で2025年最新の学習ガイド：https://www.jpntest.com/shiken/1Z0-1122-25-mondaishu
トップクラスOracle 1Z0-1122-25試験最先端学習ガイド！練習問題バージョン：https://drive.google.com/open?id=1AmUOOIerUO3H3iYto_toAMObvH0vGZbl