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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q25-Q30):

                                                  NEW QUESTION # 25
                                                  What is the benefit of using embedding models in OCI Generative AI service?

                                                  Answer: B

                                                  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 # 26
                                                  How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

                                                  Answer: C

                                                  Explanation:
                                                  In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
                                                  * Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
                                                  * Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
                                                  Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


                                                  NEW QUESTION # 27
                                                  Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                                  Answer: D

                                                  Explanation:
                                                  Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
                                                  Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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                                                  NEW QUESTION # 28
                                                  What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

                                                  Answer: D


                                                  NEW QUESTION # 29
                                                  Which AI domain can be employed for identifying patterns in images and extract relevant features?

                                                  Answer: B

                                                  Explanation:
                                                  Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.


                                                  NEW QUESTION # 30
                                                  ......

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