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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: Generative AI and Large Language Models- Generative AI Fundamentals
  • 1. Generative AI concepts and capabilities
    • 2. Generative AI use cases
      - Large Language Models
      • 1. LLM fundamentals
        • 2. Language models and generative AI applications
          Topic 2: Artificial Intelligence and Machine Learning Fundamentals- Artificial Intelligence Concepts
          • 1. AI use cases and applications
            • 2. AI fundamentals and terminology
              - 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
                      Topic 3: OCI Generative AI and Oracle Database AI Capabilities- OCI Generative AI
                      • 1. Generative AI models and applications
                        • 2. OCI Generative AI services
                          - 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
                                  Topic 4: Oracle AI and Machine Learning Services- OCI Machine Learning Services
                                  • 1. Machine learning capabilities in OCI
                                    • 2. OCI Data Science and machine learning workflows
                                      - OCI AI Services
                                      • 1. Vision
                                        • 2. Language
                                          • 3. Document Understanding
                                            • 4. Speech
                                              - Oracle AI Stack
                                              • 1. AI infrastructure
                                                • 2. AI data and machine learning services

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q30-Q35):

                                                  NEW QUESTION # 30
                                                  What is " in-context learning " in the realm of Large Language Models (LLMs)?

                                                  Answer: C

                                                  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 # 31
                                                  What is the primary purpose of reinforcement learning?

                                                  Answer: D

                                                  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 # 32
                                                  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 # 33
                                                  Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                                  Answer: D


                                                  NEW QUESTION # 34
                                                  How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

                                                  Answer: D

                                                  Explanation:
                                                  Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


                                                  NEW QUESTION # 35
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