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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Intro to Generative AI and LLMs15%- Discuss Generative AI Overview
- Explain Prompt Engineering and Instruction Tuning
- Discuss Large Language Models Fundamentals
- Explain Transformers Fundamentals
- Explain LLM Fine Tuning
Topic 2: Intro to AI Foundations10%- Explain AI vs ML vs DL
- Discuss AI Basics
- Discuss AI Applications and Types of Data
Topic 3: Get started with OCI AI Portfolio15%- Explain Responsible AI
- Discuss OCI ML Services Overview
- Discuss OCI AI Infrastructure Overview
- Discuss OCI AI Services Overview
Topic 4: Intro to OCI AI Services20%- OCI Document Understanding
- OCI Vision
- OCI Language
- OCI Speech
Topic 5: OCI Generative AI and Oracle 23ai10%- Discuss Autonomous Database Select AI
- Discuss Oracle Vector Search
- Describe OCI Generative AI Services
Topic 6: Intro to DL Foundations15%- Explain Sequence Models (RNN and LSTM)
- Discuss Deep Learning Fundamentals
- Explain Convolutional Models (CNN)
Topic 7: Intro to ML Foundations15%- Discuss Supervised Learning Fundamentals
  • 1. Classification
    • 2. Regression
      - Discuss Reinforcement Learning Fundamentals
      - Discuss Unsupervised Learning Fundamentals
      - Explain Machine Learning Basics

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q31-Q36):

      NEW QUESTION # 31
      Which capability is supported by the Oracle Cloud Infrastructure Vision service?

      Answer: C

      Explanation:
      The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.


      NEW QUESTION # 32
      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 # 33
      What feature of OCI Data Science provides an interactive coding environment for building and training models?

      Answer: A

      Explanation:
      In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.


      NEW QUESTION # 34
      Which AI domain can be employed for identifying patterns in images and extract relevant features?

      Answer: B

      Explanation:
      Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.


      NEW QUESTION # 35
      What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

      Answer: B

      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 # 36
      ......

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