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[General] Relevant 1Z0-1122-25 Answers - 1Z0-1122-25 Valid Test Pdf

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【General】 Relevant 1Z0-1122-25 Answers - 1Z0-1122-25 Valid Test Pdf

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Oracle 1Z0-1122-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.
Topic 2
  • Get started with OCI AI Portfolio: This section measures the proficiency of Cloud AI Specialists in exploring Oracle Cloud Infrastructure (OCI) AI services. It provides an overview of OCI AI and machine learning services, details AI infrastructure capabilities and explains responsible AI principles to ensure ethical and transparent AI development.
Topic 3
  • Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.
Topic 4
  • Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.
Topic 5
  • Intro to AI Foundations: This section of the exam measures the skills of AI Practitioners and Data Analysts in understanding the fundamentals of artificial intelligence. It covers key concepts, AI applications across industries, and the types of data used in AI models. It also explains the differences between artificial intelligence, machine learning, and deep learning, providing clarity on how these technologies interact and complement each other.

Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q17-Q22):NEW QUESTION # 17
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
  • A. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
  • B. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
  • C. Both involve retraining the model, but Prompt Engineering does it more often.
  • D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
Answer: D
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 # 18
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?
  • A. By analyzing sentiment in text documents
  • B. By generating lifelike speech from documents
  • C. By transcribing spoken language
  • D. By automating data extraction from documents
Answer: D
Explanation:
Explanation:

NEW QUESTION # 19
What is the primary benefit of using the OCI Language service for text analysis?
  • A. It provides image processing capabilities.
  • B. It requires extensive machine learning expertise to use.
  • C. It allows for text analysis at scale without machine learning expertise.
  • D. It only works with structured data.
Answer: C
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 # 20
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
  • A. Anomaly Detection
  • B. Natural Language Processing
  • C. Natural Language Processing
  • D. Computer Vision
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 # 21
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
  • A. They focus on increasing the number of tokens while keeping the model size constant.
  • B. They disregard model size and prioritize high-quality data only.
  • C. They ensure that the model size, training time, and data size are balanced for optimal results.
  • D. They prioritize larger model sizes to achieve better performance.
Answer: C
Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

NEW QUESTION # 22
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