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Updated: Sep 15, 2026

No. of Questions: 49 Questions & Answers with Testing Engine

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Oracle 1z0-1122-26 Exam Overview:

Certification Vendor:Oracle
Exam Name:Oracle Cloud Infrastructure 2026 AI Foundations Associate
Exam Number:1Z0-1122-26
Exam Format:Multiple Choice Questions
Available Languages:English, Japanese
Exam Price:Free
Related Certifications:Oracle Cloud Infrastructure Certified AI Foundations Associate
Real Exam Qty:40
Exam Duration:60 minutes
Certificate Validity Period:No expiration
Passing Score:65%
Sample Questions:Oracle 1z0-1122-26 Sample Questions
Exam Way:Online, unproctored exam through Oracle University.
Pre Condition:No prerequisites. No prior AI/ML knowledge or coding experience is required.
Official Syllabus URL:https://education.oracle.com/oracle-cloud-infrastructure-ai-foundations-associate/pexam_1Z0-1122-26

Oracle 1z0-1122-26 Exam Syllabus Topics:

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

                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:

                                                  Question #1

                                                  What is the benefit of using embedding models in OCI Generative AI service?

                                                  • A. They facilitate semantic searches.
                                                  • B. They optimize the use of computational resources.
                                                  • C. They enable creating detailed graphics.
                                                  • D. They simplify managing databases.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: A  🗳️

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                                                  Question #2

                                                  What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                                  • A. Conda environment
                                                  • B. Model catalog
                                                  • C. Notebook sessions
                                                  • D. Accelerated Data Science (ADS) SDK
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

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                                                  Question #3

                                                  Which AI domain can be employed for identifying patterns in images and extract relevant features?

                                                  • A. Anomaly Detection
                                                  • B. Natural Language Processing
                                                  • C. Speech Processing
                                                  • D. Computer Vision
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: D  🗳️

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                                                  Question #4

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

                                                  • A. Directly predicting the final output
                                                  • B. Capturing the internal representation of the raw image data
                                                  • C. Providing labels for the output neurons
                                                  • D. Storing the input pixel values
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: B  🗳️

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                                                  Question #5

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

                                                  • A. They focus on increasing the number of tokens while keeping the model size constant.
                                                  • B. They prioritize larger model sizes to achieve better performance.
                                                  • 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.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: D  🗳️

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