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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Generative AI and Large Language Models15%- Generative AI concepts
  • 1. Transformers, prompt engineering, and fine-tuning
    • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
      Topic 2: 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 3: AI Foundations10%- Artificial Intelligence basics and terminology
          • 1. AI applications, use cases, and responsible AI principles
            • 2. AI, Machine Learning, and Deep Learning relationship
              Topic 4: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
              • 1. Oracle 23ai Vector Database integration
                • 2. Generative AI capabilities on OCI
                  Topic 5: 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 6: 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 7: Deep Learning Foundations15%- Deep Learning and neural networks
                            • 1. Convolutional Neural Networks (CNN) architectures
                              • 2. Recurrent Neural Networks, LSTMs, and sequence models

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q21-Q26):

                                NEW QUESTION # 21
                                What would you use Oracle AI Vector Search for?

                                Answer: C

                                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 # 22
                                Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

                                Answer: C

                                Explanation:
                                Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
                                Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .


                                NEW QUESTION # 23
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: C


                                NEW QUESTION # 24
                                What is the purpose of the model catalog in OCI Data Science?

                                Answer: C

                                Explanation:
                                The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.


                                NEW QUESTION # 25
                                Which capability is supported by the Oracle Cloud Infrastructure Vision service?

                                Answer: C

                                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 # 26
                                ......

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