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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
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
      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
            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
                AI Foundations10%- Artificial Intelligence basics and terminology
                • 1. AI applications, use cases, and responsible AI principles
                  • 2. AI, Machine Learning, and Deep Learning relationship
                    Deep Learning Foundations15%- Deep Learning and neural networks
                    • 1. Recurrent Neural Networks, LSTMs, and sequence models
                      • 2. Convolutional Neural Networks (CNN) architectures
                        OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
                        • 1. Oracle 23ai Vector Database integration
                          • 2. Generative AI capabilities on OCI
                            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 (Q13-Q18):

                                NEW QUESTION # 13
                                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 # 14
                                Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

                                Answer: D

                                Explanation:
                                The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


                                NEW QUESTION # 15
                                What key objective does machine learning strive to achieve?

                                Answer: D

                                Explanation:
                                The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
                                As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


                                NEW QUESTION # 16
                                What is the primary benefit of using the OCI Language service for text analysis?

                                Answer: A

                                Explanation:
                                The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.


                                NEW QUESTION # 17
                                What would you use Oracle AI Vector Search for?

                                Answer: B

                                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 # 18
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