最新のOracle 1z0-1122-26受験準備 &合格スムーズ1z0-1122-26問題無料 |有難い1z0-1122-26日本語解説集

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
OCI AI Portfolio15%- Overview of OCI AI offerings
  • 1. AI Services, ML Services, and AI Infrastructure overview
    • 2. OCI Data Science and GPU-based compute infrastructure
      Generative AI and Large Language Models15%- Generative AI concepts
      • 1. Transformers, prompt engineering, and fine-tuning
        • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
          Deep Learning Foundations15%- Deep Learning and neural networks
          • 1. Recurrent Neural Networks, LSTMs, and sequence models
            • 2. Convolutional Neural Networks (CNN) architectures
              Machine Learning Foundations15%- Machine Learning fundamentals
              • 1. Supervised learning: regression and classification
                • 2. Reinforcement learning basics and model evaluation concepts
                  • 3. Unsupervised learning: clustering and dimensionality reduction
                    OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
                    • 1. Oracle 23ai Vector Database integration
                      • 2. Generative AI capabilities on OCI
                        AI Foundations10%- Artificial Intelligence basics and terminology
                        • 1. AI, Machine Learning, and Deep Learning relationship
                          • 2. AI applications, use cases, and responsible AI principles
                            Introduction to OCI AI Services20%- OCI AI Service APIs
                            • 1. OCI Select AI and AI service use cases
                              • 2. OCI Language, Vision, Speech, and Document Understanding services

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                                1z0-1122-26問題無料 & 1z0-1122-26日本語解説集

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate 認定 1z0-1122-26 試験問題 (Q25-Q30):

                                質問 # 25
                                Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

                                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 # 26
                                Which feature is NOT available as part of OCI Speech capabilities?

                                正解：D

                                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 # 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?

                                正解：D

                                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 does AI enhance human efforts?

                                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 # 29
                                What role do Transformers perform in Large Language Models (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.
                                References:
                                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 # 30
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