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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: OCI AI Portfolio15%- Overview of OCI AI offerings
  • 1. OCI Data Science and GPU-based compute infrastructure
    • 2. AI Services, ML Services, and AI Infrastructure overview
      Topic 2: Machine Learning Foundations15%- Machine Learning fundamentals
      • 1. Reinforcement learning basics and model evaluation concepts
        • 2. Supervised learning: regression and classification
          • 3. Unsupervised learning: clustering and dimensionality reduction
            Topic 3: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
            • 1. Oracle 23ai Vector Database integration
              • 2. Generative AI capabilities on OCI
                Topic 4: AI Foundations10%- Artificial Intelligence basics and terminology
                • 1. AI applications, use cases, and responsible AI principles
                  • 2. AI, Machine Learning, and Deep Learning relationship
                    Topic 5: 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
                        Topic 6: Deep Learning Foundations15%- Deep Learning and neural networks
                        • 1. Convolutional Neural Networks (CNN) architectures
                          • 2. Recurrent Neural Networks, LSTMs, and sequence models
                            Topic 7: Generative AI and Large Language Models15%- Generative AI concepts
                            • 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
                              • 2. Transformers, prompt engineering, and fine-tuning

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q47-Q52):

                                NEW QUESTION # 47
                                How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

                                Answer: C

                                Explanation:
                                In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
                                * Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
                                * Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
                                Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


                                NEW QUESTION # 48
                                What would you use Oracle AI Vector Search for?

                                Answer: B

                                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
                                Which is NOT a capability of OCI Vision ' s image analysis?

                                Answer: B

                                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 # 50
                                Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

                                Answer: C

                                Explanation:
                                Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model ' s accuracy over time.
                                * Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.


                                NEW QUESTION # 51
                                How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

                                Answer: B


                                NEW QUESTION # 52
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