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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Artificial Intelligence and Machine Learning Fundamentals- Deep Learning Fundamentals
  • 1. Convolutional and sequence models
    • 2. Neural networks
      - 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
              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
                      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. Document Understanding
                            • 2. Vision
                              • 3. Language
                                • 4. Speech
                                  - Oracle AI Stack
                                  • 1. AI infrastructure
                                    • 2. AI data and machine learning services
                                      OCI Generative AI and Oracle Database AI Capabilities- OCI Generative AI
                                      • 1. OCI Generative AI services
                                        • 2. Generative AI models and applications
                                          - AI Application Frameworks
                                          • 1. Language frameworks
                                            • 2. Retrieval and AI application concepts
                                              - Oracle AI Database
                                              • 1. AI capabilities in Oracle Database
                                                • 2. Vector database concepts

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q16-Q21):

                                                  NEW QUESTION # 16
                                                  You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

                                                  Answer: C

                                                  Explanation:
                                                  For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.


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

                                                  Answer: B


                                                  NEW QUESTION # 18
                                                  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 # 19
                                                  What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

                                                  Answer: B

                                                  Explanation:
                                                  The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.


                                                  NEW QUESTION # 20
                                                  Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                                  Answer: B

                                                  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 # 21
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