Pass Guaranteed 2026 Oracle Fantastic Exam 1z0-1122-26 Details

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q45-Q50):

      NEW QUESTION # 45
      How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

      Answer: A

      Explanation:
      In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
      * Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
      * Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
      Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


      NEW QUESTION # 46
      Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

      Answer: B

      Explanation:
      The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI ' s OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.


      NEW QUESTION # 47
      You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?

      Answer: D

      Explanation:
      In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
      Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.


      NEW QUESTION # 48
      Which AI Ethics principle leads to the Responsible AI requirement of transparency?

      Answer: A


      NEW QUESTION # 49
      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 # 50
      ......

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