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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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      Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q47-Q52):

      NEW QUESTION # 47
      What does " fine-tuning " refer to in the context of OCI Generative AI service?

      Answer: C

      Explanation:
      Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.


      NEW QUESTION # 48
      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 # 49
      What is the purpose of Attention Mechanism in Transformer architecture?

      Answer: C

      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 # 50
      In machine learning, what does the term " model training " mean?

      Answer: A

      Explanation:
      In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.


      NEW QUESTION # 51
      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 # 52
      ......

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