1Z0-1122-23 Oracle Cloud Infrastructure 2023 AI Foundations Associate Exam

Format: Multiple Choice
Duration: 60 Minutes
Exam Price: Free
Number of Questions: 30
Passing Score: 60%
Validation: This Exam has been validated against Oracle Cloud Infrastructure 2023
Policy: Cloud Recertification

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Passing this exam is required to earn these certifications. Select each certification title below to view full requirements.

Oracle Cloud Infrastructure 2023 AI Certified Foundations Associate

Prepare to pass exam: 1Z0-1122-23
The Oracle Cloud Infrastructure AI Foundations Associate certification is designed for individuals who intend to demonstrate fundamental knowledge of Artificial Intelligence, Machine Learning, and related services provided by Oracle Cloud Infrastructure (OCI). This certification does not mandate candidates to have data science and software engineering experience, yet familiarity with OCI basics is beneficial. This credential serves as a foundation for other OCI role-based certifications like OCI Data Science Professional or OCI Digital Assistant Professional, even though it is not a prerequisite for any of them.

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Become a OCI AI Foundations Associate (2023)
Additional Preparation and Information

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Review exam topics
Objectives % of Exam
AI Concepts and Workloads 10%
Machine Learning and Deep Learning 30%
Generative AI and Large Language Models 30%
OCI AI Infrastructure and Services 30%

AI Concepts and Workloads
Understand the fundamental AI concepts and workloads

Machine Learning and Deep Learning
Explain the key concepts and terminologies of Machine Learning
Explain the key concepts and terminologies of Deep Learning
Identify common Machine Learning types

Generative AI and Large Language Models
Understand the fundamentals of Generative AI
Explain Large Language Model concepts
Explain the role of prompt engineering and fine-tuning in Generative AI

OCI AI Infrastructure and Services
Describe OCI AI Infrastructure
Describe OCI AI Services

Sample Questions
 

QUESTION 1
In machine learning, what does the term “model training” mean?

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:
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. Reference: Oracle Cloud Infrastructure Documentation

QUESTION 2

What is the primary goal of machine learning?
A. Enabling computers to learn and improve from experience
B. Explicitly programming computers
C. Creating algorithms to solve complex problems
D. Improving computer hardware

Answer: A

Explanation:
Machine learning is a branch of artificial intelligence that enables computers to learn from data and
experience without being explicitly programmed. Machine learning algorithms can adapt to new
data and situations and improve their performance over time2. Reference: Artificial Intelligence (AI) | Oracle

QUESTION 3
What role do tokens play in Large Language Models (LLMs)?

A. They represent the numerical values of model parameters.
B. They are used to define the architecture of the model’s neural network.
C. They are Individual units into which a piece of text is divided during processing by the model.
D. They determine the size of the model’s memory.

Answer: C

Explanation:
Tokens are the basic units of text representation in large language models. They can be words,
subwords, characters, or symbols. Tokens are used to encode the input text into numerical vectors
that can be processed by the models neural network. Tokens also determine the vocabulary size and
the maximum sequence length of the model3. Reference: Oracle Cloud Infrastructure 2023 AI
Foundations Associate | Oracle University

QUESTION 4
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They ensure that the model size, training time, and data size are balanced for optimal results.
B. They disregard model size and prioritize high-quality data only.
C. They focus on increasing the number of tokens while keeping the model size constant.
D. They prioritize larger model sizes to achieve better performance.

Answer: D

Explanation:
Large language models are trained on massive amounts of data to capture the complexity and
diversity of natural language. Larger model sizes mean more parameters, which enable the model to
learn more patterns and nuances from the data. Larger models also tend to generalize better to new
tasks and domains. However, larger models also require more computational resources, data quality,
and data size to train and deploy. Therefore, large language models handle the trade-off by
prioritizing larger model sizes to achieve better performance, while using various techniques to
optimize the training and inference efficiency4. Reference: Artificial Intelligence (AI) | Oracle

Students Feed Back
 

Ugorji Chinyere United Kingdom 1 months ago
Just passed yesterday (12-oct 2023), thank you Examtopics
upvoted 29 times

Hamza Naeem Voted 2 years, 1 week ago – Lahore – Pakistan
Verified- passed today with 80+. Most questions are covered here. Thank you Certkingdom.
upvoted 15 times

Plenty David 1 month ago – Woodbridge – Virginia
Just passed mith 90%. 50 Qeustions. only 2 not part of this dump. With Dump and discussions you will be well prepared.
upvoted 21 times


Abdul Hamid 1 month, 2 weeks ago – Jalan Tenaga, Singapore
Amazing exam questions.
Totally helpful
upvoted 31 times

NQWENISO 2 months ago – South Africa
Great source! Always read discussion to verify the answers!
upvoted 1 times

Zeynep Aslan 2 months, 3 weeks ago – Turkey
Just passed the exam with a score of 911. Most of these questions come in the exam
upvoted 3 times

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