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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Oracle AI and Machine Learning Services- OCI AI Services
  • 1. Language
    • 2. Speech
      • 3. Document Understanding
        • 4. Vision
          - OCI Machine Learning Services
          • 1. OCI Data Science and machine learning workflows
            • 2. Machine learning capabilities in OCI
              - Oracle AI Stack
              • 1. AI data and machine learning services
                • 2. AI infrastructure
                  Generative AI and Large Language Models- Generative AI Fundamentals
                  • 1. Generative AI use cases
                    • 2. Generative AI concepts and capabilities
                      - Large Language Models
                      • 1. Language models and generative AI applications
                        • 2. LLM fundamentals
                          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. Convolutional and sequence models
                                    • 2. Neural networks
                                      OCI Generative AI and Oracle Database AI Capabilities- 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
                                              - 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 (Q15-Q20):

                                                  NEW QUESTION # 15
                                                  Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

                                                  Answer: B

                                                  Explanation:
                                                  Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model ' s accuracy over time.
                                                  * Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.


                                                  NEW QUESTION # 16
                                                  What are Convolutional Neural Networks (CNNs) primarily used for?

                                                  Answer: C

                                                  Explanation:
                                                  Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
                                                  CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.


                                                  NEW QUESTION # 17
                                                  Which feature is NOT available as part of OCI Speech capabilities?

                                                  Answer: A

                                                  Explanation:
                                                  OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
                                                  These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.


                                                  NEW QUESTION # 18
                                                  In machine learning, what does the term " model training " mean?

                                                  Answer: B

                                                  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 # 19
                                                  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: A

                                                  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 # 20
                                                  ......

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