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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: Oracle AI and Machine Learning Services- Oracle AI Stack
  • 1. AI data and machine learning services
    • 2. AI infrastructure
      - OCI Machine Learning Services
      • 1. Machine learning capabilities in OCI
        • 2. OCI Data Science and machine learning workflows
          - OCI AI Services
          • 1. Speech
            • 2. Document Understanding
              • 3. Language
                • 4. Vision
                  Topic 2: Artificial Intelligence and Machine Learning Fundamentals- Machine Learning Fundamentals
                  • 1. Supervised and unsupervised learning
                    • 2. Machine learning models and architectures
                      - Deep Learning Fundamentals
                      • 1. Convolutional and sequence models
                        • 2. Neural networks
                          - Artificial Intelligence Concepts
                          • 1. AI fundamentals and terminology
                            • 2. AI use cases and applications
                              Topic 3: Generative AI and Large Language Models- Generative AI Fundamentals
                              • 1. Generative AI concepts and capabilities
                                • 2. Generative AI use cases
                                  - Large Language Models
                                  • 1. Language models and generative AI applications
                                    • 2. LLM fundamentals
                                      Topic 4: OCI Generative AI and Oracle Database AI Capabilities- OCI Generative AI
                                      • 1. OCI Generative AI services
                                        • 2. Generative AI models and applications
                                          - AI Application Frameworks
                                          • 1. Language frameworks
                                            • 2. Retrieval and AI application concepts
                                              - Oracle AI Database
                                              • 1. Vector database concepts
                                                • 2. AI capabilities in Oracle Database

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q19-Q24):

                                                  NEW QUESTION # 19
                                                  You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?

                                                  Answer: C

                                                  Explanation:
                                                  In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.


                                                  NEW QUESTION # 20
                                                  What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                                  Answer: A

                                                  Explanation:
                                                  Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.


                                                  NEW QUESTION # 21
                                                  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: C

                                                  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 # 22
                                                  In machine learning, what does the term " model training " mean?

                                                  Answer: D

                                                  Explanation:
                                                  In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.


                                                  NEW QUESTION # 23
                                                  What is the primary purpose of reinforcement learning?

                                                  Answer: B

                                                  Explanation:
                                                  Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


                                                  NEW QUESTION # 24
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