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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: 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
          Topic 3: 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 4: 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
                    Topic 5: Deep Learning Foundations15%- Deep Learning and neural networks
                    • 1. Convolutional Neural Networks (CNN) architectures
                      • 2. Recurrent Neural Networks, LSTMs, and sequence models
                        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 (Q45-Q50):

                                NEW QUESTION # 45
                                You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

                                Answer: B

                                Explanation:
                                For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.


                                NEW QUESTION # 46
                                Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

                                Answer: B

                                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 # 47
                                You are working on a multilingual public announcement system. Which AI task will you use to implement it?

                                Answer: A

                                Explanation:
                                For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
                                Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .


                                NEW QUESTION # 48
                                What is the difference between classification and regression in Supervised Machine Learning?

                                Answer: B

                                Explanation:
                                In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .


                                NEW QUESTION # 49
                                What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                Answer: C

                                Explanation:
                                In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.


                                NEW QUESTION # 50
                                ......

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