Exam: 1Z0-1122-23
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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
Earn associated certifications
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.
Take recommended training
Complete one of the courses below to prepare for your exam (optional):
Become a OCI AI Foundations Associate (2023)
Additional Preparation and Information
A combination of Oracle training and hands-on experience (attained via labs
and/or field experience), in the learning subscription, provides the best
preparation for passing the exam.
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
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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
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