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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q18-Q23):

                                NEW QUESTION # 18
                                What is " in-context learning " in the realm of Large Language Models (LLMs)?

                                Answer: A

                                Explanation:
                                " In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
                                This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.


                                NEW QUESTION # 19
                                What is the difference between classification and regression in Supervised Machine Learning?

                                Answer: B

                                Explanation:
                                In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .


                                NEW QUESTION # 20
                                How does AI enhance human efforts?

                                Answer: C

                                Explanation:
                                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.


                                NEW QUESTION # 21
                                What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                Answer: C

                                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 # 22
                                What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

                                Answer: C

                                Explanation:
                                In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


                                NEW QUESTION # 23
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