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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q37-Q42):

      NEW QUESTION # 37
      What can Oracle Cloud Infrastructure Document Understanding NOT do?

      Answer: C

      Explanation:
      Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .


      NEW QUESTION # 38
      You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

      Answer: B

      Explanation:
      For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.


      NEW QUESTION # 39
      Which capability is supported by the Oracle Cloud Infrastructure Vision service?

      Answer: D

      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 # 40
      What key objective does machine learning strive to achieve?

      Answer: D

      Explanation:
      The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
      As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


      NEW QUESTION # 41
      How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

      Answer: A

      Explanation:
      Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


      NEW QUESTION # 42
      ......

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