Oracle 1z0-1122-26 : Oracle Cloud Infrastructure 2026 AI Foundations Associate

  • Exam Code: 1z0-1122-26
  • Exam Name: Oracle Cloud Infrastructure 2026 AI Foundations Associate
  • Updated: Sep 13, 2026     Q & A: 49 Questions and Answers

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:

                                Question #1

                                What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                • A. Doubling the neural network layers
                                • B. Adjusting the model parameters to improve accuracy
                                • C. Upgrading the hardware of the AI clusters
                                • D. Encrypting the data for security reasons
                                Reveal Solution  Discussion  0

                                Correct Answer: B  🗳️

                                Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).

                                Question #2

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

                                • A. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
                                • B. Both involve retraining the model, but Prompt Engineering does it more often.
                                • C. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
                                • D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
                                Reveal Solution  Discussion  0

                                Correct Answer: D  🗳️

                                Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).

                                Question #3

                                You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?

                                • A. Increase the threshold value for classification.
                                • B. Increase the regularization parameter.
                                • C. Decrease the weight of the feature representing the presence of certain keywords.
                                • D. Decrease the number of iterations.
                                Reveal Solution  Discussion  0

                                Correct Answer: A  🗳️

                                Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).

                                Question #4

                                What is the purpose of Attention Mechanism in Transformer architecture?

                                • A. Weigh the importance of different words within a sequence and understand the context.
                                • B. Convert tokens into numerical forms (vectors) that the model can understand.
                                • C. Break down a sentence into smaller pieces called tokens.
                                • D. Apply a specific function to each word individually.
                                Reveal Solution  Discussion  0

                                Correct Answer: A  🗳️

                                Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).

                                Question #5

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

                                • A. Time series prediction
                                • B. Text processing
                                • C. Image generation
                                • D. Image classification
                                Reveal Solution  Discussion  0

                                Correct Answer: D  🗳️

                                Explanation: Only visible for Prep4sures members. You can sign-up / login (it's free).

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