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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: OCI Generative AI and Oracle Database AI Capabilities- 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
          - OCI Generative AI
          • 1. Generative AI models and applications
            • 2. OCI Generative AI services
              Topic 2: 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 data and machine learning services
                    • 2. AI infrastructure
                      - OCI AI Services
                      • 1. Speech
                        • 2. Vision
                          • 3. Document Understanding
                            • 4. Language
                              Topic 3: Generative AI and Large Language Models- Generative AI Fundamentals
                              • 1. Generative AI concepts and capabilities
                                • 2. Generative AI use cases
                                  - Large Language Models
                                  • 1. LLM fundamentals
                                    • 2. Language models and generative AI applications
                                      Topic 4: Artificial Intelligence and Machine Learning Fundamentals- Deep Learning Fundamentals
                                      • 1. Neural networks
                                        • 2. Convolutional and sequence models
                                          - Artificial Intelligence Concepts
                                          • 1. AI fundamentals and terminology
                                            • 2. AI use cases and applications
                                              - Machine Learning Fundamentals
                                              • 1. Machine learning models and architectures
                                                • 2. Supervised and unsupervised learning

                                                  >> Valid 1z0-1122-26 Exam Question <<

                                                  Exam 1z0-1122-26 Preparation, New 1z0-1122-26 Exam Practice

                                                  TestPassed's Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) exam questions contain Oracle 1z0-1122-26 real questions and answers that have been compiled and verified by Oracle specialists in the field. This demonstrates that the real questions and answers in the Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) material are legitimate for the Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) practice exam. The Oracle 1z0-1122-26 practice questions are intended to help you easily and confidently clear the Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26).

                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q44-Q49):

                                                  NEW QUESTION # 44
                                                  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: D

                                                  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 # 45
                                                  Which feature is NOT available as part of OCI Speech capabilities?

                                                  Answer: B

                                                  Explanation:
                                                  OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
                                                  These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.


                                                  NEW QUESTION # 46
                                                  What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

                                                  Answer: A


                                                  NEW QUESTION # 47
                                                  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: A

                                                  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 # 48
                                                  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 # 49
                                                  ......

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