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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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

      NEW QUESTION # 31
      Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?

      Answer: A

      Explanation:
      Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
      Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
      ' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.


      NEW QUESTION # 32
      Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

      Answer: C

      Explanation:
      Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.


      NEW QUESTION # 33
      What is the purpose of Attention Mechanism in Transformer architecture?

      Answer: D

      Explanation:
      The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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      NEW QUESTION # 34
      You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?

      Answer: A

      Explanation:
      In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.


      NEW QUESTION # 35
      Which feature is NOT available as part of OCI Speech capabilities?

      Answer: C

      Explanation:
      OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
      These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.


      NEW QUESTION # 36
      ......

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