Exam: 1Z0-1122-24
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Oracle Cloud Infrastructure 2024 Certified AI Foundations Associate
Format: Multiple Choice
Duration: 60 Minutes
Exam Price: Free
Number of Questions: 40
Passing Score: 65%
Validation: This Exam has been validated against Oracle Cloud Infrastructure
2024
Policy: Cloud Recertification
Prepare to pass exam: 1Z0-1122-24
The Oracle Cloud Infrastructure (OCI) AI Foundations certification is
designed to introduce learners to the fundamental concepts of artificial
intelligence (AI) and machine learning (ML), with a specific focus on the
practical application of these technologies within the Oracle Cloud
Infrastructure. This course is ideal for beginners and provides an accessible
entry point for those looking to enhance their understanding of AI and ML
without the requirement of prior extensive technical experience.
By participating in this course, you will gain a comprehensive overview of the
AI landscape, including an understanding of basic AI and ML concepts, deep
learning fundamentals, and the role of generative AI and large language models
in modern computing. The course is structured to ensure a step-by-step learning
process, guiding you from the basic principles to more complex topics in AI,
making learning both effective and engaging.
Take recommended training
Complete one of the courses below to prepare for your exam (optional):
Become An OCI AI Foundations Associate (2024)
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
Intro to AI Foundations 10%
Intro to ML Foundations 15%
Intro to DL Foundations 15%
Intro to Generative AI & LLMs 15%
Get started with OCI AI Portfolio 15%
OCI Generative AI and Oracle 23ai 10%
Intro to OCI AI Services* 20%
Intro to AI Foundations
Discuss AI Basics
Discuss AI Applications & Types of Data
Explain AI vs ML vs DL
Intro to ML Foundations
Explain Machine Learning Basics
Discuss Supervised Learning Fundamentals (Regression & Classification)
Discuss Unsupervised Learning Fundamentals
Discuss Reinforcement Learning Fundamentals
Intro to DL Foundations
Discuss Deep Learning Fundamentals
Explain Convolutional Models (CNN)
Explain Sequence Models (RNN & LSTM)
Intro to Generative AI & LLMs
Discuss Generative AI Overview
Discuss Large Language Models Fundamentals
Explain Transformers Fundamentals
Explain Prompt Engineering & Instruction Tuning
Explain LLM Fine Tuning
Get started with OCI AI Portfolio
Discuss OCI AI Services Overview
Discuss OCI ML Services Overview
Discuss OCI AI Infrastructure Overview
Explain Responsible AI
OCI Generative AI and Oracle 23ai
Describe OCI Generative AI Services
Discuss Autonomous Database Select AI
Discuss Oracle Vector Search
Intro to OCI AI Services*
Explore OCI AI Services & related APIs (Language, Vision, Document
Understanding, Speech)
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QUESTION 1
What is the key feature of Recurrent Neural Networks (RNNs)?
A. They process data in parallel.
B. They are primarily used for image recognition tasks.
C. They have a feedback loop that allows information to persist across different
time steps.
D. They do not have an internal state.
Answer: C
Explanation:
Recurrent Neural Networks (RNNs) are a class of neural networks where
connections between nodes
can form cycles. This cycle creates a feedback loop that allows the network to
maintain an internal
state or memory, which persists across different time steps. This is the key
feature of RNNs that
distinguishes them from other neural networks, such as feedforward neural
networks that process
inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information
is important, such as
in language modeling, time-series prediction, and speech recognition. The
ability to retain
information from previous inputs enables RNNs to make more informed predictions
based on the
entire sequence of data, not just the current input.
In contrast:
Option A (They process data in parallel) is incorrect because RNNs typically
process data sequentially, not in parallel.
Option B (They are primarily used for image recognition tasks) is incorrect
because image recognition
is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
Option D (They do not have an internal state) is incorrect because having an
internal state is a
defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to
handle sequences of
data effectively by "remembering" past inputs to influence future outputs. This
memory capability is
what makes RNNs powerful for applications that involve sequential or
time-dependent data .
QUESTION 2
What role do Transformers perform in Large Language Models (LLMs)?
A. Limit the ability of LLMs to handle large datasets by imposing strict memory
constraints
B. Manually engineer features in the data before training the model
C. Provide a mechanism to process sequential data in parallel and capture
long-range dependencies
D. Image recognition tasks in LLMs
Answer: C
Explanation:
Transformers play a critical role in Large Language Models (LLMs), like GPT-4,
by providing an
efficient and effective mechanism to process sequential data in parallel while
capturing long-range
dependencies. This capability is essential for understanding and generating
coherent and
contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of
data one step at a
time, which can be slow and difficult to scale. In contrast, Transformers allow
for the parallel
processing of sequences, significantly speeding up the computation and making it
feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables
the model to
consider all parts of the input data simultaneously, rather than sequentially.
Each token (word,
punctuation, etc.) in the sequence is compared with every other token, allowing
the model to weigh
the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is
crucial for
understanding context in natural language processing tasks. For example, in a
long sentence or
paragraph, the meaning of a word can depend on other words that are far apart in
the sequence. The
self-attention mechanism in Transformers allows the model to capture these
dependencies
effectively by focusing on relevant parts of the text regardless of their
position in the sequence.
This ability to capture long-range dependencies enhances the model's
understanding of context,
leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows
these models to
generate text that is not only contextually appropriate but also maintains
coherence across long
passages, which is a significant improvement over earlier models. This is why
the Transformer is the
foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they
enable parallel
processing and capture long-range dependencies, which are essential for
effective language
understanding and generation .
QUESTION 3
Which is NOT a category of pretrained foundational models available in the
OCI Generative AI service?
A. Embedding models
B. Translation models
C. Chat models
D. Generation models
Answer: B
Explanation:
The OCI Generative AI service offers various categories of pretrained
foundational models, including
Embedding models, Chat models, and Generation models. These models are designed
to perform a
wide range of tasks, such as generating text, answering questions, and providing
contextual
embeddings. However, Translation models, which are typically used for converting
text from one
language to another, are not a category available in the OCI Generative AI
service's current offerings.
The focus of the OCI Generative AI service is more aligned with tasks related to
text generation, chat
interactions, and embedding generation rather than direct language translation .
QUESTION 4
What does "fine-tuning" refer to in the context of OCI Generative AI
service?
A. Encrypting the data for security reasons
B. Adjusting the model parameters to improve accuracy
C. Upgrading the hardware of the AI clusters
D. Doubling the neural network layers
Answer: B
Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the
process of adjusting the
parameters of a pretrained model to better fit a specific task or dataset. This
process involves further
training the model on a smaller, task-specific dataset, allowing the model to
refine its understanding
and improve its performance on that specific task. Fine-tuning is essential for
customizing the
general capabilities of a pretrained model to meet the particular needs of a
given application,
resulting in more accurate and relevant outputs. It is distinct from other
processes like encrypting
data, upgrading hardware, or simply increasing the complexity of the model
architecture .
QUESTION 5
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster
for AI workloads?
A. It delivers exceptional performance and scalability for complex AI tasks.
B. It is ideal for tasks such as text-to-speech conversion.
C. It offers seamless integration with social media platforms.
D. It provides a cost-effective solution for simple AI tasks.
Answer: A
Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional
performance and
scalability for complex AI tasks. The primary benefit of this infrastructure is
its ability to handle
demanding AI workloads, offering high-performance computing (HPC) capabilities
that are crucial for
training large-scale AI models and processing massive datasets. The architecture
of the Supercluster
ensures low-latency networking, efficient resource allocation, and
high-throughput processing,
making it ideal for AI tasks that require significant computational power, such
as deep learning, data
analytics, and large-scale simulations .
QUESTION 6
Which AI Ethics principle leads to the Responsible AI requirement of
transparency?
A. Explicability
B. Prevention of harm
C. Respect for human autonomy
D. Fairness
Answer: A
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