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

1z0-1122-26 testking pdf

Exam Code: 1z0-1122-26

Exam Name: Oracle Cloud Infrastructure 2026 AI Foundations Associate

Updated: Sep 08, 2026

Q & A: 49 Questions and Answers

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1z0-1122-26 exam dumps

Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
  • 1. Oracle 23ai Vector Database integration
    • 2. Generative AI capabilities on OCI
      Topic 2: Generative AI and Large Language Models15%- Generative AI concepts
      • 1. Transformers, prompt engineering, and fine-tuning
        • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
          Topic 3: Deep Learning Foundations15%- Deep Learning and neural networks
          • 1. Recurrent Neural Networks, LSTMs, and sequence models
            • 2. Convolutional Neural Networks (CNN) architectures
              Topic 4: 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 5: 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 6: 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
                            Topic 7: 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

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


                                Question 2

                                Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

                                A. Chat models
                                B. Generation models
                                C. Translation models
                                D. Embedding models


                                Question 3

                                You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?

                                A. Regression
                                B. Clustering
                                C. Binary Classification
                                D. Multi-Class Classification


                                Question 4

                                What would you use Oracle AI Vector Search for?

                                A. Manage database security protocols.
                                B. Store business data in a cloud database.
                                C. Query data based on keywords.
                                D. Query data based on semantics.


                                Question 5

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

                                A. They prioritize larger model sizes to achieve better performance.
                                B. They focus on increasing the number of tokens while keeping the model size constant.
                                C. They disregard model size and prioritize high-quality data only.
                                D. They ensure that the model size, training time, and data size are balanced for optimal results.


                                Solutions:

                                Question 1
                                Answer: A
                                Question 2
                                Answer: C
                                Question 3
                                Answer: D
                                Question 4
                                Answer: D
                                Question 5
                                Answer: D

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