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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q42-Q47):

                                NEW QUESTION # 42
                                What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                Answer: C

                                Explanation:
                                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.


                                NEW QUESTION # 43
                                You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                Answer: C

                                Explanation:
                                A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.


                                NEW QUESTION # 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?

                                Answer: D

                                Explanation:
                                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.


                                NEW QUESTION # 45
                                What would you use Oracle AI Vector Search for?

                                Answer: B

                                Explanation:
                                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 .


                                NEW QUESTION # 46
                                What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                Answer: A

                                Explanation:
                                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.


                                NEW QUESTION # 47
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