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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. Unsupervised learning: clustering and dimensionality reduction
      • 3. Supervised learning: regression and classification
        AI Foundations10%- Artificial Intelligence basics and terminology
        • 1. AI, Machine Learning, and Deep Learning relationship
          • 2. AI applications, use cases, and responsible AI principles
            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
                    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 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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q12-Q17):

                                NEW QUESTION # 12
                                Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

                                Answer: D

                                Explanation:
                                The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service ' s current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.


                                NEW QUESTION # 13
                                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 # 14
                                What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

                                Answer: A

                                Explanation:
                                The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.


                                NEW QUESTION # 15
                                Which capability is supported by the Oracle Cloud Infrastructure Vision service?

                                Answer: A

                                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 # 16
                                What is " in-context learning " in the realm of Large Language Models (LLMs)?

                                Answer: D

                                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 # 17
                                ......

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