Quiz 2026 Oracle 1z0-1122-26: Oracle Cloud Infrastructure 2026 AI Foundations Associate Useful Clear Exam

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
OCI AI Portfolio15%- Overview of OCI AI offerings
  • 1. OCI Data Science and GPU-based compute infrastructure
    • 2. AI Services, ML Services, and AI Infrastructure overview
      OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
      • 1. Oracle 23ai Vector Database integration
        • 2. Generative AI capabilities on OCI
          AI Foundations10%- Artificial Intelligence basics and terminology
          • 1. AI applications, use cases, and responsible AI principles
            • 2. AI, Machine Learning, and Deep Learning relationship
              Machine Learning Foundations15%- Machine Learning fundamentals
              • 1. Supervised learning: regression and classification
                • 2. Reinforcement learning basics and model evaluation concepts
                  • 3. Unsupervised learning: clustering and dimensionality reduction
                    Introduction to OCI AI Services20%- OCI AI Service APIs
                    • 1. OCI Select AI and AI service use cases
                      • 2. OCI Language, Vision, Speech, and Document Understanding services
                        Generative AI and Large Language Models15%- Generative AI concepts
                        • 1. Transformers, prompt engineering, and fine-tuning
                          • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
                            Deep Learning Foundations15%- Deep Learning and neural networks
                            • 1. Convolutional Neural Networks (CNN) architectures
                              • 2. Recurrent Neural Networks, LSTMs, and sequence models

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q37-Q42):

                                NEW QUESTION # 37
                                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 She initially tries fine-tuning but finds that the model still uses outdated information.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: B

                                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 # 38
                                Which feature of OCI Speech helps make transcriptions easier to read and understand?

                                Answer: A

                                Explanation:
                                The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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                                NEW QUESTION # 39
                                What is the primary purpose of reinforcement learning?

                                Answer: A

                                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 # 40
                                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 # 41
                                What key objective does machine learning strive to achieve?

                                Answer: B

                                Explanation:
                                The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
                                As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


                                NEW QUESTION # 42
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