1z0-1122-23 Dumps - Oracle Cloud Infrastructure 2023 AI Foundations Associate
Note! Following 1z0-1122-23 Exam is Retired now. Please select the alternative replacement for your Exam Certification.
The new exam code is 1z0-1122-24
Oracle Cloud Infrastructure Vision is a serverless, multi-tenant service, accessible using the Console, or over REST APIs. You can upload images to detect and classify objects in them. If you have lots of images, you can process them in batch using asynchronous API endpoints. Vision’s features are thematically split between Document AI for document-centric images, and Image Analysis for object and scene-based images. Image Analysis supports both pretrained and custom models for object detection and image classification3. References: Vision - Oracle
Question 2
In machine learning, what does the term "model training" mean?
Options:
A.
Analyzing the accuracy of a trained model
B.
Establishing a relationship between Input features and output
C.
Writing code for the entire program
D.
Performing data analysis on collected and labeled data
Answer:
B
Explanation:
Explanation:
Model training is the process of finding the optimal values for the model parameters that minimize the error between the model predictions and the actual output. This is done by using a learning algorithm that iteratively updates the parameters based on the input features and the output1. References: Oracle Cloud Infrastructure Documentation
Question 3
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?
Options:
A.
Customizes the model architecture
B.
Trains a model from scratch
C.
Guides the model's response using predefined prompts
D.
Involves post-processing model outputs and optimizinghyper parameters
Answer:
C
Explanation:
Explanation:
Prompt engineering is the art of designing natural language instructions or queries that can elicit the desired response from a large language model. Prompt engineering does not modify the model parameters or architecture, but rather relies on the model’s existing knowledge and capabilities. Prompt engineering can be used to perform various tasks such as text generation, sentiment analysis, and code completion, by providing the model with the appropriate context, format, and constraints67. Prompt engineering is also known as zero-shot learning or query-based learning. References: [2211.01910] Large Language Models Are Human-Level Prompt Engineers] A developer’s guide to prompt engineering and LLMs - The GitHub Blog