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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: Artificial Intelligence and Machine Learning Fundamentals- Machine Learning Fundamentals
  • 1. Machine learning models and architectures
    • 2. Supervised and unsupervised learning
      - 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
              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. Language models and generative AI applications
                    • 2. LLM fundamentals
                      Topic 3: OCI Generative AI and Oracle Database AI Capabilities- Oracle AI Database
                      • 1. AI capabilities in Oracle Database
                        • 2. Vector database concepts
                          - AI Application Frameworks
                          • 1. Language frameworks
                            • 2. Retrieval and AI application concepts
                              - OCI Generative AI
                              • 1. Generative AI models and applications
                                • 2. OCI Generative AI services
                                  Topic 4: Oracle AI and Machine Learning Services- Oracle AI Stack
                                  • 1. AI infrastructure
                                    • 2. AI data and machine learning services
                                      - OCI Machine Learning Services
                                      • 1. OCI Data Science and machine learning workflows
                                        • 2. Machine learning capabilities in OCI
                                          - OCI AI Services
                                          • 1. Speech
                                            • 2. Language
                                              • 3. Vision
                                                • 4. Document Understanding

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q44-Q49):

                                                  NEW QUESTION # 44
                                                  Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

                                                  Answer: C

                                                  Explanation:
                                                  The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


                                                  NEW QUESTION # 45
                                                  What is the key feature of Recurrent Neural Networks (RNNs)?

                                                  Answer: C

                                                  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 # 46
                                                  You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?

                                                  Answer: C

                                                  Explanation:
                                                  In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.


                                                  NEW QUESTION # 47
                                                  You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                                  Answer: C

                                                  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 # 48
                                                  What key objective does machine learning strive to achieve?

                                                  Answer: C

                                                  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 # 49
                                                  ......

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