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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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 use cases
        • 2. Generative AI concepts and capabilities
          Topic 2: OCI Generative AI and Oracle Database AI Capabilities- OCI Generative AI
          • 1. Generative AI models and applications
            • 2. OCI Generative AI services
              - 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
                      Topic 3: Artificial Intelligence and Machine Learning Fundamentals- 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
                              - Deep Learning Fundamentals
                              • 1. Neural networks
                                • 2. Convolutional and sequence models
                                  Topic 4: Oracle AI and Machine Learning Services- OCI Machine Learning Services
                                  • 1. OCI Data Science and machine learning workflows
                                    • 2. Machine learning capabilities in OCI
                                      - Oracle AI Stack
                                      • 1. AI infrastructure
                                        • 2. AI data and machine learning services
                                          - OCI AI Services
                                          • 1. Language
                                            • 2. Speech
                                              • 3. Document Understanding
                                                • 4. Vision

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q38-Q43):

                                                  NEW QUESTION # 38
                                                  What key objective does machine learning strive to achieve?

                                                  Answer: A

                                                  Explanation:
                                                  The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
                                                  As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


                                                  NEW QUESTION # 39
                                                  Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?

                                                  Answer: A

                                                  Explanation:
                                                  OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
                                                  Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
                                                  Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.


                                                  NEW QUESTION # 40
                                                  What is the purpose of the model catalog in OCI Data Science?

                                                  Answer: D

                                                  Explanation:
                                                  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.


                                                  NEW QUESTION # 41
                                                  Which feature of OCI Speech helps make transcriptions easier to read and understand?

                                                  Answer: A

                                                  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 # 42
                                                  Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

                                                  Answer: C

                                                  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 # 43
                                                  ......

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