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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

      >> Standard 1z0-1122-26 Answers <<

      Oracle Cloud Infrastructure 2026 AI Foundations Associate Pass Cert & 1z0-1122-26 Actual Questions & Oracle Cloud Infrastructure 2026 AI Foundations Associate Training Vce

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q25-Q30):

      NEW QUESTION # 25
      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 # 26
      Which AI Ethics principle leads to the Responsible AI requirement of transparency?

      Answer: D


      NEW QUESTION # 27
      Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

      Answer: D

      Explanation:
      The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service ' s current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.


      NEW QUESTION # 28
      What can Oracle Cloud Infrastructure Document Understanding NOT do?

      Answer: D

      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 # 29
      What does " fine-tuning " refer to in the context of OCI Generative AI service?

      Answer: A

      Explanation:
      Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.


      NEW QUESTION # 30
      ......

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