Oracle 1Z0-1122-25 Exam Questions - Navigate Your Path to Success

The Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) exam is a good choice for AI Practitioners and Data Analysts and if the candidate manages to pass Oracle Cloud Infrastructure 2025 AI Foundations Associate exam, he/she will earn Oracle Cloud , Oracle Cloud Infrastructure Certifications. Below are some essential facts for Oracle 1Z0-1122-25 exam candidates:

In actual Oracle Cloud Infrastructure 2025 AI Foundations Associate (1Z0-1122-25) 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 1Z0-1122-25 syllabus.
Our Oracle 1Z0-1122-25 Exam Practice Questions were last updated on: Apr 16, 2025

Sample Questions for Oracle 1Z0-1122-25 Exam Preparation

Question 1

What is the primary purpose of reinforcement learning?

AFinding relationships within data sets

BIdentifying patterns in data

CMaking predictions from labeled data

DLearning from outcomes to make decisions

Correct : D

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

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Question 2

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

AWriting code for the entire program

BPerforming data analysis on collected and labeled data

CAnalyzing the accuracy of a trained model

DEstablishing a relationship between input features and output

Correct : D

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.