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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Machine Learning Foundations15%- Machine Learning fundamentals
  • 1. Supervised learning: regression and classification
    • 2. Reinforcement learning basics and model evaluation concepts
      • 3. Unsupervised learning: clustering and dimensionality reduction
        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: AI Foundations10%- Artificial Intelligence basics and terminology
            • 1. AI, Machine Learning, and Deep Learning relationship
              • 2. AI applications, use cases, and responsible AI principles
                Topic 4: Generative AI and Large Language Models15%- Generative AI concepts
                • 1. Transformers, prompt engineering, and fine-tuning
                  • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
                    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: Deep Learning Foundations15%- Deep Learning and neural networks
                        • 1. Recurrent Neural Networks, LSTMs, and sequence models
                          • 2. Convolutional Neural Networks (CNN) architectures
                            Topic 7: 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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q36-Q41):

                                NEW QUESTION # 36
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: B


                                NEW QUESTION # 37
                                What would you use Oracle AI Vector Search for?

                                Answer: D

                                Explanation:
                                Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .


                                NEW QUESTION # 38
                                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 # 39
                                Which feature of OCI Speech helps make transcriptions easier to read and understand?

                                Answer: D

                                Explanation:
                                The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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                                NEW QUESTION # 40
                                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?

                                Answer: A

                                Explanation:
                                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.


                                NEW QUESTION # 41
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