1z0-1122-26 Certification Exam Infor, 1z0-1122-26 Exam Questions Answers

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  Oracle 1z0-1122-26 Exam Questions Answers & New 1z0-1122-26 Exam Testking

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q48-Q53):

                                                  NEW QUESTION # 48
                                                  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 # 49
                                                  What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

                                                  Answer: D


                                                  NEW QUESTION # 50
                                                  What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                                  Answer: B

                                                  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 # 51
                                                  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: D

                                                  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 # 52
                                                  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 # 53
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