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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q12-Q17):

                                                  NEW QUESTION # 12
                                                  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 # 13
                                                  You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

                                                  Answer: C

                                                  Explanation:
                                                  A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.


                                                  NEW QUESTION # 14
                                                  What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

                                                  Answer: A

                                                  Explanation:
                                                  In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


                                                  NEW QUESTION # 15
                                                  Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

                                                  Answer: C

                                                  Explanation:
                                                  Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
                                                  Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
                                                  ' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.


                                                  NEW QUESTION # 16
                                                  Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

                                                  Answer: D

                                                  Explanation:
                                                  The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


                                                  NEW QUESTION # 17
                                                  ......

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