Latest 1z0-1122-26 Exam Online - 1z0-1122-26 Exam Passing Score

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q40-Q45):

                                                  NEW QUESTION # 40
                                                  You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                                  Answer: D

                                                  Explanation:
                                                  A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.


                                                  NEW QUESTION # 41
                                                  How does AI enhance human efforts?

                                                  Answer: D

                                                  Explanation:
                                                  AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.


                                                  NEW QUESTION # 42
                                                  What is the difference between classification and regression in Supervised Machine Learning?

                                                  Answer: C

                                                  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 # 43
                                                  What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                                  Answer: C

                                                  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 # 44
                                                  What distinguishes Generative AI from other types of AI?

                                                  Answer: C

                                                  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 # 45
                                                  ......

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