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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Artificial Intelligence and Machine Learning Fundamentals- Deep Learning Fundamentals
  • 1. Neural networks
    • 2. Convolutional and sequence models
      - Artificial Intelligence Concepts
      • 1. AI use cases and applications
        • 2. AI fundamentals and terminology
          - Machine Learning Fundamentals
          • 1. Supervised and unsupervised learning
            • 2. Machine learning models and architectures
              OCI Generative AI and Oracle Database AI Capabilities- OCI Generative AI
              • 1. OCI Generative AI services
                • 2. Generative AI models and applications
                  - Oracle AI Database
                  • 1. Vector database concepts
                    • 2. AI capabilities in Oracle Database
                      - AI Application Frameworks
                      • 1. Language frameworks
                        • 2. Retrieval and AI application concepts
                          Oracle AI and Machine Learning Services- Oracle AI Stack
                          • 1. AI data and machine learning services
                            • 2. AI infrastructure
                              - OCI AI Services
                              • 1. Speech
                                • 2. Document Understanding
                                  • 3. Vision
                                    • 4. Language
                                      - OCI Machine Learning Services
                                      • 1. Machine learning capabilities in OCI
                                        • 2. OCI Data Science and machine learning workflows
                                          Generative AI and Large Language Models- Large Language Models
                                          • 1. Language models and generative AI applications
                                            • 2. LLM fundamentals
                                              - Generative AI Fundamentals
                                              • 1. Generative AI concepts and capabilities
                                                • 2. Generative AI use cases

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q34-Q39):

                                                  NEW QUESTION # 34
                                                  Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                                  Answer: A

                                                  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 # 35
                                                  What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

                                                  Answer: B


                                                  NEW QUESTION # 36
                                                  Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

                                                  Answer: B

                                                  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 # 37
                                                  Which capability is supported by Oracle Cloud Infrastructure Language service?

                                                  Answer: A

                                                  Explanation:
                                                  Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.


                                                  NEW QUESTION # 38
                                                  What are Convolutional Neural Networks (CNNs) primarily used for?

                                                  Answer: A

                                                  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 # 39
                                                  ......

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