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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: 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
            Topic 3: 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 4: 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
                    Topic 5: Deep Learning Foundations15%- Deep Learning and neural networks
                    • 1. Recurrent Neural Networks, LSTMs, and sequence models
                      • 2. Convolutional Neural Networks (CNN) architectures
                        Topic 6: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
                        • 1. Generative AI capabilities on OCI
                          • 2. Oracle 23ai Vector Database integration
                            Topic 7: Generative AI and Large Language Models15%- Generative AI concepts
                            • 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
                              • 2. Transformers, prompt engineering, and fine-tuning

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q27-Q32):

                                NEW QUESTION # 27
                                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 # 28
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: A


                                NEW QUESTION # 29
                                You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                Answer: B

                                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 # 30
                                Which capability is supported by the Oracle Cloud Infrastructure Vision service?

                                Answer: B

                                Explanation:
                                The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.


                                NEW QUESTION # 31
                                Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

                                Answer: C

                                Explanation:
                                Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
                                In this hierarchy:
                                * AI includes all methods to make machines intelligent.
                                * ML refers to the methods within AI that focus on learning from data.
                                * DL is a specialized field within ML that deals with deep neural networks.


                                NEW QUESTION # 32
                                ......

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