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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q28-Q33):

      NEW QUESTION # 28
      Which is NOT a capability of OCI Vision ' s image analysis?

      Answer: A

      Explanation:
      OCI Vision ' s image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision ' s image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision ' s core image analysis features.
      Top of Form
      Bottom of Form


      NEW QUESTION # 29
      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 # 30
      You are working on a multilingual public announcement system. Which AI task will you use to implement it?

      Answer: A

      Explanation:
      For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
      Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .


      NEW QUESTION # 31
      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: C

      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 # 32
      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 # 33
      ......

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