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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  100% Pass Quiz 2026 Professional Oracle Official 1z0-1122-26 Practice Test

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q22-Q27):

                                                  NEW QUESTION # 22
                                                  Which is NOT a capability of OCI Vision ' s image analysis?

                                                  Answer: C

                                                  Explanation:
                                                  OCI Vision ' s image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision ' s image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision ' s core image analysis features.
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                                                  NEW QUESTION # 23
                                                  Which AI domain can be employed for identifying patterns in images and extract relevant features?

                                                  Answer: C

                                                  Explanation:
                                                  Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.


                                                  NEW QUESTION # 24
                                                  How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

                                                  Answer: B

                                                  Explanation:
                                                  Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


                                                  NEW QUESTION # 25
                                                  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 # 26
                                                  Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

                                                  Answer: D

                                                  Explanation:
                                                  The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service ' s current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.


                                                  NEW QUESTION # 27
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