CertJukenはその近道を提供し、君の多くの時間と労力も節約します。CertJukenはOracleの1z0-1122-26認定試験「Oracle Cloud Infrastructure 2026 AI Foundations Associate」に向けてもっともよい問題集を研究しています。もしほかのホームページに弊社みたいな問題集を見れば、あとでみ続けて、弊社の商品を盗作することとよくわかります。CertJukenが提供した資料は最も全面的で、しかも更新の最も速いです。
| Section | Weight | Objectives |
|---|---|---|
| Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning - Discuss Generative AI Overview - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services |
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Document Understanding - OCI Vision - OCI Language |
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics |
| Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview |
| Intro to AI Foundations | 10% | - Discuss AI Applications and Types of Data - Discuss AI Basics - Explain AI vs ML vs DL |
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質問 # 46
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
正解：D
解説：
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
質問 # 47
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
正解：C
解説：
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.
質問 # 48
What feature of OCI Data Science provides an interactive coding environment for building and training models?
正解：A
解説：
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
質問 # 49
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients ' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?
正解：A
解説：
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.
質問 # 50
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
正解：B
解説：
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 .
質問 # 51
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