信頼できる1z0-1122-26模擬トレーリング &合格スムーズ1z0-1122-26専門試験 |一番優秀な1z0-1122-26認証試験

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Foundations10%- Artificial Intelligence basics and terminology
  • 1. AI, Machine Learning, and Deep Learning relationship
    • 2. AI applications, use cases, and responsible AI principles
      Topic 2: Machine Learning Foundations15%- Machine Learning fundamentals
      • 1. Unsupervised learning: clustering and dimensionality reduction
        • 2. Supervised learning: regression and classification
          • 3. Reinforcement learning basics and model evaluation concepts
            Topic 3: Introduction to OCI AI Services20%- OCI AI Service APIs
            • 1. OCI Language, Vision, Speech, and Document Understanding services
              • 2. OCI Select AI and AI service use cases
                Topic 4: Generative AI and Large Language Models15%- Generative AI concepts
                • 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
                  • 2. Transformers, prompt engineering, and fine-tuning
                    Topic 5: 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
                        Topic 6: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
                        • 1. Oracle 23ai Vector Database integration
                          • 2. Generative AI capabilities on OCI
                            Topic 7: Deep Learning Foundations15%- Deep Learning and neural networks
                            • 1. Recurrent Neural Networks, LSTMs, and sequence models
                              • 2. Convolutional Neural Networks (CNN) architectures

                                >> 1z0-1122-26模擬トレーリング <<

                                1z0-1122-26専門試験、1z0-1122-26認証試験

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate 認定 1z0-1122-26 試験問題 (Q40-Q45):

                                質問 # 40
                                Which is NOT a capability of OCI Vision ' s image analysis?

                                正解：C

                                解説：
                                OCI Vision ' s image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision ' s image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision ' s core image analysis features.
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                                質問 # 41
                                What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                正解：D

                                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 # 42
                                What would you use Oracle AI Vector Search for?

                                正解：B

                                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 # 43
                                Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

                                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 # 44
                                Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

                                正解：C

                                解説：
                                Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
                                Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
                                ' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.


                                質問 # 45
                                ......

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                                1z0-1122-26専門試験: https://www.it-passports.com/1z0-1122-26.html