1Z0-1122-25難易度受験料、1Z0-1122-25最新関連参考書弊社の1Z0-1122-25練習資料は、さまざまな学位の受験者に適しています。この受験者は、この分野の知識のレベルに関係なく互換性があります。 これらのOracle Cloud Infrastructure 2025 AI Foundations Associateトレーニング資料は、当社にとって名誉あるものであり、お客様の目標を達成するための最大の特権として扱っています。 一方、理論と実践を離婚することはできませんが、心配する必要はありません。刺激テストの質問があり、学習と実践の両方を同時に行うことができます。 私たちの知る限り、1Z0-1122-25試験準備は何百万人もの受験者に夢を追いかけ、より効率的に学習するように動機付けました。 1Z0-1122-25の練習資料は、あなたを失望させません。 Oracle Cloud Infrastructure 2025 AI Foundations Associate 認定 1Z0-1122-25 試験問題 (Q35-Q40):質問 # 35
What distinguishes Generative AI from other types of AI?
A. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
B. Generative AI uses algorithms to predict outcomes based on past data.
C. Generative AI involves training models to perform tasks without human intervention.
D. Generative AI focuses on making decisions based on user interactions.
正解：A
解説：
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.
質問 # 36
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
A. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
B. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
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.
正解：D
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 # 37
What is the difference between classification and regression in Supervised Machine Learning?
A. Classification and regression both assign data points to categories.
B. Classification and regression both predict continuous values.
C. Classification predicts continuous values, whereas regression assigns data points to categories.
D. Classification assigns data points to categories, whereas regression predicts continuous values.
正解：D
解説：
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".
質問 # 38
What feature of OCI Data Science provides an interactive coding environment for building and training models?
A. Notebook sessions
B. Accelerated Data Science (ADS) SDK
C. Model catalog
D. Conda environment
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 # 39
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
A. Natural Language Processing
B. Anomaly Detection
C. Natural Language Processing
D. Computer Vision
正解：A
解説：
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.