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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
  • 1. Oracle 23ai Vector Database integration
    • 2. Generative AI capabilities on OCI
      Generative AI and Large Language Models15%- Generative AI concepts
      • 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
        • 2. Transformers, prompt engineering, and fine-tuning
          Introduction to OCI AI Services20%- OCI AI Service APIs
          • 1. OCI Language, Vision, Speech, and Document Understanding services
            • 2. OCI Select AI and AI service use cases
              OCI AI Portfolio15%- Overview of OCI AI offerings
              • 1. AI Services, ML Services, and AI Infrastructure overview
                • 2. OCI Data Science and GPU-based compute infrastructure
                  AI Foundations10%- Artificial Intelligence basics and terminology
                  • 1. AI applications, use cases, and responsible AI principles
                    • 2. AI, Machine Learning, and Deep Learning relationship
                      Machine Learning Foundations15%- Machine Learning fundamentals
                      • 1. Reinforcement learning basics and model evaluation concepts
                        • 2. Unsupervised learning: clustering and dimensionality reduction
                          • 3. Supervised learning: regression and classification
                            Deep Learning Foundations15%- Deep Learning and neural networks
                            • 1. Convolutional Neural Networks (CNN) architectures
                              • 2. Recurrent Neural Networks, LSTMs, and sequence models

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q43-Q48):

                                43. Frage
                                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?

                                Antwort: D

                                Begründung:
                                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.


                                44. Frage
                                You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                Antwort: C

                                Begründung:
                                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.


                                45. Frage
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Antwort: D

                                Begründung:
                                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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                                46. Frage
                                What is the purpose of the model catalog in OCI Data Science?

                                Antwort: A

                                Begründung:
                                The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.


                                47. Frage
                                You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?

                                Antwort: B

                                Begründung:
                                In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
                                Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.


                                48. Frage
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