1z0-1122-26問題無料 & 1z0-1122-26参考書内容

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Introduction to OCI AI Services	20%	- OCI AI Service APIs 1. OCI Language, Vision, Speech, and Document Understanding services 2. OCI Select AI and AI service use cases
Topic 2: Generative AI and Large Language Models	15%	- Generative AI concepts 1. Transformers, prompt engineering, and fine-tuning 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
Topic 3: OCI AI Portfolio	15%	- Overview of OCI AI offerings 1. AI Services, ML Services, and AI Infrastructure overview 2. OCI Data Science and GPU-based compute infrastructure
Topic 4: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
Topic 5: OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Generative AI capabilities on OCI 2. Oracle 23ai Vector Database integration
Topic 6: AI Foundations	10%	- Artificial Intelligence basics and terminology 1. AI applications, use cases, and responsible AI principles 2. AI, Machine Learning, and Deep Learning relationship
Topic 7: Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Supervised learning: regression and classification 2. Reinforcement learning basics and model evaluation concepts 3. Unsupervised learning: clustering and dimensionality reduction

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Oracle 1z0-1122-26参考書内容 & 1z0-1122-26模擬試験

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Oracle Cloud Infrastructure 2026 AI Foundations Associate 認定 1z0-1122-26 試験問題 (Q21-Q26):

質問 # 21
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?

A. It converts audio files into text.
B. It recognizes and extracts text from a document.
C. It enhances the visual quality of documents.
D. It provides real-time translation of text.

正解：B

解説：
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI ' s OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.

質問 # 22
What is the purpose of Attention Mechanism in Transformer architecture?

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

正解：C

解説：
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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質問 # 23
How does AI enhance human efforts?

A. By completely replacing human workers in all tasks
B. By increasing the physical strength of humans
C. By processing data at a speed and effectiveness far beyond human capability
D. By deleting data humans need to handle

正解：C

解説：
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.

質問 # 24
In machine learning, what does the term " model training " mean?

A. Performing data analysis on collected and labeled data
B. Writing code for the entire program
C. Establishing a relationship between input features and output
D. Analyzing the accuracy of a trained model

正解：C

解説：
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.

質問 # 25
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.

正解：B

解説：
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.

質問 # 26
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