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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
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 Generative AI and Oracle 23ai10%- OCI Generative AI Service features
      • 1. Generative AI capabilities on OCI
        • 2. Oracle 23ai Vector Database integration
          Deep Learning Foundations15%- Deep Learning and neural networks
          • 1. Convolutional Neural Networks (CNN) architectures
            • 2. Recurrent Neural Networks, LSTMs, and sequence models
              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
                  Machine Learning Foundations15%- Machine Learning fundamentals
                  • 1. Unsupervised learning: clustering and dimensionality reduction
                    • 2. Reinforcement learning basics and model evaluation concepts
                      • 3. Supervised learning: regression and classification
                        Generative AI and Large Language Models15%- Generative AI concepts
                        • 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
                          • 2. Transformers, prompt engineering, and fine-tuning
                            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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q36-Q41):

                                NEW QUESTION # 36
                                What are Convolutional Neural Networks (CNNs) primarily used for?

                                Answer: B

                                Explanation:
                                Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
                                CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.


                                NEW QUESTION # 37
                                What is the primary purpose of reinforcement learning?

                                Answer: D

                                Explanation:
                                Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


                                NEW QUESTION # 38
                                What is " in-context learning " in the realm of Large Language Models (LLMs)?

                                Answer: C

                                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 # 39
                                What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

                                Answer: B

                                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 # 40
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: C


                                NEW QUESTION # 41
                                ......

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