Oracle 1z0-1122-26 Test Pdf, Certification 1z0-1122-26 Exam

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
AI Foundations10%- Artificial Intelligence basics and terminology
  • 1. AI applications, use cases, and responsible AI principles
    • 2. AI, Machine Learning, and Deep Learning relationship
      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
          OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
          • 1. Oracle 23ai Vector Database integration
            • 2. Generative AI capabilities on OCI
              Deep Learning Foundations15%- Deep Learning and neural networks
              • 1. Convolutional Neural Networks (CNN) architectures
                • 2. Recurrent Neural Networks, LSTMs, and sequence models
                  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
                        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
                            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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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q29-Q34):

                                NEW QUESTION # 29
                                What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                Answer: D

                                Explanation:
                                In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.


                                NEW QUESTION # 30
                                Which capability is supported by the Oracle Cloud Infrastructure Vision service?

                                Answer: D

                                Explanation:
                                The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.


                                NEW QUESTION # 31
                                Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

                                Answer: D

                                Explanation:
                                Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
                                Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .


                                NEW QUESTION # 32
                                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 # 33
                                What is the primary purpose of reinforcement learning?

                                Answer: B

                                Explanation:
                                Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


                                NEW QUESTION # 34
                                ......

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