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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Deep Learning Foundations15%- Deep Learning and neural networks
  • 1. Convolutional Neural Networks (CNN) architectures
    • 2. Recurrent Neural Networks, LSTMs, and sequence models
      Topic 2: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
      • 1. Oracle 23ai Vector Database integration
        • 2. Generative AI capabilities on OCI
          Topic 3: Introduction to OCI AI Services20%- OCI AI Service APIs
          • 1. OCI Select AI and AI service use cases
            • 2. OCI Language, Vision, Speech, and Document Understanding services
              Topic 4: AI Foundations10%- Artificial Intelligence basics and terminology
              • 1. AI applications, use cases, and responsible AI principles
                • 2. AI, Machine Learning, and Deep Learning relationship
                  Topic 5: OCI AI Portfolio15%- Overview of OCI AI offerings
                  • 1. OCI Data Science and GPU-based compute infrastructure
                    • 2. AI Services, ML Services, and AI Infrastructure overview
                      Topic 6: Machine Learning Foundations15%- Machine Learning fundamentals
                      • 1. Unsupervised learning: clustering and dimensionality reduction
                        • 2. Supervised learning: regression and classification
                          • 3. Reinforcement learning basics and model evaluation concepts
                            Topic 7: Generative AI and Large Language Models15%- Generative AI concepts
                            • 1. Transformers, prompt engineering, and fine-tuning
                              • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs

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                                Quiz 1z0-1122-26 - Oracle Cloud Infrastructure 2026 AI Foundations Associate Accurate Test Cram

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q24-Q29):

                                NEW QUESTION # 24
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: C

                                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 # 25
                                You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                Answer: A

                                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 # 26
                                How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

                                Answer: A

                                Explanation:
                                In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
                                * Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
                                * Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
                                Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


                                NEW QUESTION # 27
                                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: B

                                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 # 28
                                What can Oracle Cloud Infrastructure Document Understanding NOT do?

                                Answer: A

                                Explanation:
                                Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .


                                NEW QUESTION # 29
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