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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      1z0-1122-26 Exam Introduction, 1z0-1122-26 Examcollection Vce

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q13-Q18):

      NEW QUESTION # 13
      How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

      Answer: B

      Explanation:
      Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.


      NEW QUESTION # 14
      What is " in-context learning " in the realm of Large Language Models (LLMs)?

      Answer: C

      Explanation:
      " In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
      This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.


      NEW QUESTION # 15
      Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

      Answer: C

      Explanation:
      Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model ' s accuracy over time.
      * Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.


      NEW QUESTION # 16
      Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

      Answer: A

      Explanation:
      Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
      Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .


      NEW QUESTION # 17
      Which feature is NOT available as part of OCI Speech capabilities?

      Answer: A

      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 # 18
      ......

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