Latest 1z0-1122-26 Practice Materials | 1z0-1122-26 Detailed Study Dumps

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q46-Q51):

                                                  NEW QUESTION # 46
                                                  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 # 47
                                                  What is the difference between classification and regression in Supervised Machine Learning?

                                                  Answer: D

                                                  Explanation:
                                                  In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .


                                                  NEW QUESTION # 48
                                                  What is the key feature of Recurrent Neural Networks (RNNs)?

                                                  Answer: A

                                                  Explanation:
                                                  Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
                                                  RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
                                                  In contrast:
                                                  * Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
                                                  * Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
                                                  * Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
                                                  This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by " remembering " past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.


                                                  NEW QUESTION # 49
                                                  What distinguishes Generative AI from other types of AI?

                                                  Answer: D

                                                  Explanation:
                                                  Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.


                                                  NEW QUESTION # 50
                                                  Which feature of OCI Speech helps make transcriptions easier to read and understand?

                                                  Answer: C

                                                  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 # 51
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