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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q44-Q49):

      NEW QUESTION # 44
      What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

      Answer: C

      Explanation:
      In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


      NEW QUESTION # 45
      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: B

      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 # 46
      What is the primary benefit of using the OCI Language service for text analysis?

      Answer: A

      Explanation:
      The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.


      NEW QUESTION # 47
      Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

      Answer: B

      Explanation:
      Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
      In this hierarchy:
      * AI includes all methods to make machines intelligent.
      * ML refers to the methods within AI that focus on learning from data.
      * DL is a specialized field within ML that deals with deep neural networks.


      NEW QUESTION # 48
      What is the difference between classification and regression in Supervised Machine Learning?

      Answer: D

      Explanation:
      In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .


      NEW QUESTION # 49
      ......

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