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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
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
      Deep Learning Foundations15%- Deep Learning and neural networks
      • 1. Recurrent Neural Networks, LSTMs, and sequence models
        • 2. Convolutional Neural Networks (CNN) architectures
          Generative AI and Large Language Models15%- Generative AI concepts
          • 1. Transformers, prompt engineering, and fine-tuning
            • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
              OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
              • 1. Oracle 23ai Vector Database integration
                • 2. Generative AI capabilities on OCI
                  Machine Learning Foundations15%- Machine Learning fundamentals
                  • 1. Reinforcement learning basics and model evaluation concepts
                    • 2. Unsupervised learning: clustering and dimensionality reduction
                      • 3. Supervised learning: regression and classification
                        Introduction to OCI AI Services20%- OCI AI Service APIs
                        • 1. OCI Language, Vision, Speech, and Document Understanding services
                          • 2. OCI Select AI and AI service use cases
                            AI Foundations10%- Artificial Intelligence basics and terminology
                            • 1. AI applications, use cases, and responsible AI principles
                              • 2. AI, Machine Learning, and Deep Learning relationship

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q28-Q33):

                                NEW QUESTION # 28
                                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: C

                                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 # 29
                                What are Convolutional Neural Networks (CNNs) primarily used for?

                                Answer: D

                                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 # 30
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: C


                                NEW QUESTION # 31
                                Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

                                Answer: D

                                Explanation:
                                Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.


                                NEW QUESTION # 32
                                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 # 33
                                ......

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