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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: Artificial Intelligence and Machine Learning Fundamentals- Artificial Intelligence Concepts
  • 1. AI fundamentals and terminology
    • 2. AI use cases and applications
      - 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 2: OCI Generative AI and Oracle Database AI Capabilities- Oracle AI Database
              • 1. AI capabilities in Oracle Database
                • 2. Vector database concepts
                  - OCI Generative AI
                  • 1. OCI Generative AI services
                    • 2. Generative AI models and applications
                      - AI Application Frameworks
                      • 1. Retrieval and AI application concepts
                        • 2. Language frameworks
                          Topic 3: Oracle AI and Machine Learning Services- Oracle AI Stack
                          • 1. AI infrastructure
                            • 2. AI data 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. Document Understanding
                                    • 2. Language
                                      • 3. Speech
                                        • 4. Vision
                                          Topic 4: 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

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q24-Q29):

                                                  NEW QUESTION # 24
                                                  What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                                  Answer: D

                                                  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 # 25
                                                  Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

                                                  Answer: C

                                                  Explanation:
                                                  Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.


                                                  NEW QUESTION # 26
                                                  How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

                                                  Answer: C

                                                  Explanation:
                                                  Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.


                                                  NEW QUESTION # 27
                                                  What is the benefit of using embedding models in OCI Generative AI service?

                                                  Answer: C

                                                  Explanation:
                                                  Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
                                                  The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .


                                                  NEW QUESTION # 28
                                                  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 # 29
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