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Free PDF Accurate Oracle - Latest 1Z0-1122-25 Exam Guide

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Oracle 1Z0-1122-25 Exam Syllabus Topics:

Topic
Details

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

OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.

Topic 3

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 4

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.

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Free PDF 2026 Oracle 1Z0-1122-25: Oracle Cloud Infrastructure 2025 AI Foundations Associate –Professional Latest Exam Guide

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q16-Q21):

NEW QUESTION # 16
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?

A. Both involve retraining the model, but Prompt Engineering does it more often.
B. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
C. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
D. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.

Answer: B

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 # 17
Which AI domain can be employed for identifying patterns in images and extract relevant features?

A. Natural Language Processing
B. Computer Vision
C. Anomaly Detection
D. Speech Processing

Answer: B

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 18
What is the purpose of Attention Mechanism in Transformer architecture?

A. Weigh the importance of different words within a sequence and understand the context.
B. Apply a specific function to each word individually.
C. Break down a sentence into smaller pieces called tokens.
D. Convert tokens into numerical forms (vectors) that the model can understand.

Answer: A

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 # 19
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By analyzing sentiment in text documents
B. By transcribing spoken language
C. By generating lifelike speech from documents
D. By automating data extraction from documents

Answer: D

Explanation:
Explanation:

NEW QUESTION # 20
What is the primary benefit of using the OCI Language service for text analysis?

A. It only works with structured data.
B. It provides image processing capabilities.
C. It requires extensive machine learning expertise to use.
D. It allows for text analysis at scale without machine learning expertise.

Answer: D

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 # 21
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