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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q14-Q19):

                                                  NEW QUESTION # 14
                                                  Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

                                                  Answer: C

                                                  Explanation:
                                                  Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
                                                  Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
                                                  ' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.


                                                  NEW QUESTION # 15
                                                  How does AI enhance human efforts?

                                                  Answer: D

                                                  Explanation:
                                                  AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.


                                                  NEW QUESTION # 16
                                                  What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                                  Answer: C

                                                  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 # 17
                                                  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 # 18
                                                  Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                                  Answer: A


                                                  NEW QUESTION # 19
                                                  ......

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