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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q30-Q35):

      NEW QUESTION # 30
      What can Oracle Cloud Infrastructure Document Understanding NOT do?

      Answer: A

      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 # 31
      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 # 32
      Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?

      Answer: D

      Explanation:
      OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
      Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
      Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.


      NEW QUESTION # 33
      Which AI domain can be employed for identifying patterns in images and extract relevant features?

      Answer: D

      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 # 34
      What distinguishes Generative AI from other types of AI?

      Answer: B

      Explanation:
      Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.


      NEW QUESTION # 35
      ......

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