Oracle 1Z0-1122-24 Exam Questions - Navigate Your Path to Success

The Oracle Cloud Infrastructure 2024 AI Foundations Associate (1Z0-1122-24) exam is a good choice for Oracle System Administrators Oracle Cloud Architects and if the candidate manages to pass Oracle Cloud Infrastructure 2024 AI Foundations Associate exam, he/she will earn Oracle Cloud , Oracle Cloud Infrastructure Certifications. Below are some essential facts for Oracle 1Z0-1122-24 exam candidates:

In actual Oracle Cloud Infrastructure 2024 AI Foundations Associate (1Z0-1122-24) exam, a candidate can expect 40 Questions and the officially allowed time is expected to be around 60 Minutes.
TrendyCerts offers 41 Questions that are based on actual Oracle 1Z0-1122-24 syllabus.
Our Oracle 1Z0-1122-24 Exam Practice Questions were last updated on: Mar 06, 2025

Sample Questions for Oracle 1Z0-1122-24 Exam Preparation

Question 1

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

APrompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

BBoth involve retraining the model, but Prompt Engineering does it more often.

CPrompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.

DPrompt Engineering modifies training data, while Fine-tuning alters the model's structure.

Correct : A

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.

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Question 2

Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

AReinforcement learning

BUnsupervised learning

CActive learning

DSupervised learning

Correct : B

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 .