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1Z0-1122-24 Exam
Oracle Cloud Infrastructure
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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.
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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
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What are Convolutional Neural Networks (CNNs) primarily used for?
A. Image classification
B. Text processing
C. Image generation
D. Time series prediction
Answer: A
Explanation:
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks
involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their
ability to detect features such as edges, textures, and shapes across multiple layers of convolutional
filters. This makes them the model of choice for tasks such as object recognition, image segmentation,
and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their
primary application remains in image classification.
Question: 5
Which feature of OCI Speech helps make transcriptions easier to read and understand?
A. Audio tuning
B. Timestamping
C. Profanity filtering
D. Text normalization
Answer: D
Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand
by converting spoken language into a more standardized and grammatically correct format. This process
includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear,
accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions,
making them more accessible and easier to process in downstream applications.
Question: 6
What is the primary purpose of Convolutional Neural Networks (CNNs)?
A. Processing sequential data
B. Generating Images
C. Creating music compositions
D. Detecting patterns in images
Answer: D
Explanation:
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Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that is particularly well-
suited for image recognition and processing tasks. They are made up of multiple layers, including
convolutional layers, pooling layers, and fully connected layers. The convolutional layer is the core
building block of a CNN, and it is where the majority of computation occurs. It requires a few
components, which are input data, a filter, and a feature map. The filter is a small matrix of weights that
slides over the input data and performs element-wise multiplication and summation, resulting in a feature
map that represents the activation of a certain feature in the input. By applying multiple filters, the CNN
can detect different patterns in the image, such as edges, shapes, colors, textures, etc. The pooling layer
is used to reduce the spatial dimensionality of the feature maps, while preserving the most important
information. The fully connected layer is the final layer of a CNN, and it is where the classification or
regression task is performed based on the extracted features. CNNs can learn to detect complex
patterns in images by adjusting their weights during training using backpropagation and gradient descent
algorithms. Reference: : Convolutional neural network - Wikipedia, What are Convolutional Neural
Networks? | IBM, Convolutional Neural Network (CNN) in Machine Learning
Question: 7
Which Oracle Cloud Infrastructure (OCI) AI Service would be the most appropriate to implement if you
need to analyze the content of business contracts to extract key information like dates, names, and
monetary values?
A. OCI Language Service
B. OCI Data Science Service
C. OCI Document Understanding Service
D. OCI Vision Service
Answer: C
Explanation:
The OCI Document Understanding Service is specifically designed to analyze and extract structured
information from documents, making it ideal for processing business contracts and retrieving key data
points such as dates, names, and monetary values.
Question: 8
In the context of deploying large language models (LLMs) in Oracle Cloud Infrastructure's AI services,
which of the following statements are correct? (Select two)
A. Large Language Models are designed to process structured datasets and are highly optimized for
tabular data, like traditional machine learning models.
B. LLMs are only capable of handling a fixed context window size, meaning they can’t process
sequences of text longer than a set number of tokens.
C. LLMs in Oracle Cloud Infrastructure are limited to supporting English-language tasks, as multilingual
support is not feasible for models of this size.
D. Transformer architecture forms the backbone of modern LLMs, allowing the models to capture long-
range dependencies in text effectively.
E. LLMs use deep neural networks with billions of parameters and are pretrained on large corpora of
text data to generate human-like text.
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Answer: D, E
Explanation:
LLMs use deep neural networks with billions of parameters and are pretrained on large corpora of text
data, enabling them to generate human-like text effectively.
Transformer architecture is fundamental to modern LLMs, allowing these models to capture long-range
dependencies in text, which enhances their understanding and generation capabilities.
Question: 9
Which component of the OCI Data Science service is responsible for the training and evaluation of
machine learning models, allowing data scientists to define their training environments and compute
resources?
A. Pre-built Models
B. Notebook Sessions
C. Data Labeling Service
D. Model Deployment
Answer: B
Explanation:
The component of the OCI Data Science service responsible for the training and evaluation of machine
learning models is Notebook Sessions. This feature allows data scientists to define their training
environments, configure compute resources, and conduct experiments interactively.
Question: 10
You are developing an application to provide real-time language translation and sentiment analysis on
customer feedback recordings. Which OCI AI services should you integrate to achieve both
functionalities?
A. OCI Language Service and OCI Speech Service
B. OCI Language Service and OCI Document Understanding Service
C. OCI Vision Service and OCI Data Science Service
D. OCI Document Understanding Service and OCI Speech Service
Answer: A
Explanation:
To achieve real-time language translation and sentiment analysis on customer feedback recordings, you
should integrate the OCI Language Service and OCI Speech Service. The Language Service handles
sentiment analysis, while the Speech Service provides language translation capabilities.
Question: 11
Which of the following statements accurately describe the operation and architecture of Large Language
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Models (LLMs) in Oracle Cloud Infrastructure's AI services? (Select two)
A. Fine-tuning LLMs involves updating the entire model architecture and parameters to optimize for
specific tasks.
B. LLMs are designed to generate only factual information, minimizing the risk of generating biased or
incorrect outputs.
C. LLMs are pre-trained models designed to understand and generate human language, typically
trained on vast amounts of text data using self-supervised learning.
D. Once an LLM is trained, it can only perform a single task like text classification or translation,
requiring separate models for different tasks.
E. LLMs rely on Transformer architecture, where attention mechanisms allow the model to focus on
specific parts of input data to handle long-range dependencies.
Answer: C, E
Explanation:
The statements that accurately describe the operation and architecture of Large Language Models
(LLMs) in Oracle Cloud Infrastructure's AI services are:
C: LLMs are pre-trained models designed to understand and generate human language, typically trained
on vast amounts of text data using self-supervised learning. This foundational training allows them to
perform a variety of language tasks.
E: LLMs rely on Transformer architecture, where attention mechanisms allow the model to focus on
specific parts of input data to handle long-range dependencies. This capability is crucial for
understanding context and generating coherent responses in natural language.
Question: 12
Which two statements accurately describe the limitations or challenges of Generative AI models,
particularly large language models (LLMs)? (Select two)
A. Generative AI models are more prone to overfitting compared to traditional machine learning
models, making them unsuitable for most real-world applications.
B. Large language models (LLMs) like GPT can generate biased outputs due to the biases present in
their training data.
C. Generative AI models cannot be fine-tuned after their initial training phase, which limits their
adaptability to specific tasks.
D. Generative AI models, such as large language models, can generate outputs that sometimes
contain factual inaccuracies or hallucinations.
E. Generative AI models require significant amounts of labeled training data to function properly.
Answer: B, D
Explanation:
The two statements that accurately describe the limitations or challenges of Generative AI models,
particularly large language models (LLMs), are:
B: Large language models (LLMs) like GPT can generate biased outputs due to the biases present in
their training data. This can lead to unintended and harmful consequences in real-world applications.
D: Generative AI models, such as large language models, can generate outputs that sometimes contain
factual inaccuracies or hallucinations, making it essential to verify the information they provide.
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Question: 13
Which of the following statements accurately explains the role of attention heads in the self-attention
mechanism of transformer models used in Generative AI?
A. The self-attention mechanism always assigns higher weights to tokens that are closer together in
the input sequence.
B. Multiple attention heads allow transformer models to process and interpret multiple relationships
between tokens in parallel.
C. Attention heads in transformer models only focus on the first word in the input sequence, ensuring
proper context for subsequent words.
D. Attention heads in transformer models only contribute to the training phase and are not utilized
during inference.
Answer: B
Explanation:
The role of attention heads in the self-attention mechanism of transformer models is that multiple
attention heads allow transformer models to process and interpret multiple relationships between tokens
in parallel. This parallel processing capability enhances the model's ability to understand the context and
relationships within the input data.
Question: 14
Which of the following best describes the relationship between Artificial Intelligence (AI), Machine
Learning (ML), and Deep Learning (DL)?
A. AI is a subset of ML, and ML is a subset of DL.
B. AI is a subset of DL, and DL is a subset of ML.
C. DL is a subset of ML, and ML is a subset of AI.
D. ML and AI are two independent concepts with no overlap, while DL is a subset of AI.
Answer: C
Explanation:
The best description of the relationship between Artificial Intelligence (AI), Machine Learning (ML), and
Deep Learning (DL) is C: DL is a subset of ML, and ML is a subset of AI. This hierarchy illustrates that
while all deep learning is machine learning, not all machine learning is deep learning, and all machine
learning falls under the broader category of AI.
Question: 15
Which of the following statements accurately describe the characteristics of deep learning architectures
in the context of artificial neural networks? (Select two)
A. Recurrent Neural Networks (RNNs) are specialized for handling tasks that involve temporal or
sequential information.
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B. Deep learning models use multiple layers of neurons to automatically learn features from raw data.
C. Convolutional Neural Networks (CNNs) are designed primarily for processing sequential data such
as time series or natural language.
D. Autoencoders are a type of deep learning model used for unsupervised learning and dimensionality
reduction.
E. Deep learning models typically require very little data to generalize well to new, unseen data.
Answer: A, B
Explanation:
The statements that accurately describe the characteristics of deep learning architectures in the context
of artificial neural networks are:
A: Recurrent Neural Networks (RNNs) are specialized for handling tasks that involve temporal or
sequential information. This makes them suitable for tasks like language modeling and time series
prediction.
B: Deep learning models use multiple layers of neurons to automatically learn features from raw data.
This ability to learn hierarchical representations is a hallmark of deep learning architectures.
Question: 16
Which of the following best describes the concept of a reward function in reinforcement learning?
A. The reward function provides the agent with a model of the environment to learn from.
B. The reward function is used to generate synthetic data for training the agent in a simulated
environment.
C. The reward function defines a sequence of actions that lead to the optimal solution in a
reinforcement learning problem.
D. The reward function gives the agent feedback based on its actions, helping it to optimize its policy
over time.
Answer: D
Explanation:
The concept of a reward function in reinforcement learning is best described as the reward function gives
the agent feedback based on its actions, helping it to optimize its policy over time. This feedback is
crucial for the agent to learn from its experiences and improve its decision-making.
Question: 17
Your team has built a machine learning model in OCI Data Science and now needs to deploy it to a
production environment for real-time inference. Which two of the following services should you use to
deploy and monitor the model? (Select two)
A. OCI API Gateway
B. OCI AI Vision
C. OCI Functions
D. OCI AI Language
E. OCI Monitoring
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Answer: C, E
Explanation:
To deploy and monitor a machine learning model built in OCI Data Science for real-time inference, the
two services you should use are OCI Functions and OCI Monitoring. OCI Functions allows you to deploy
serverless functions for the model, while OCI Monitoring helps track the model's performance and
operational metrics in production.
Question: 18
What is the primary advantage of using Oracle Cloud Infrastructure (OCI) AI services like OCI Vision or
OCI Language, compared to using the OCI Data Science service for machine learning applications?
A. OCI AI services allow developers to train custom machine learning models with minimal effort
B. OCI AI services offer pre-built, ready-to-use models for common AI tasks like image recognition and
language processing.
C. OCI AI services are designed to allow data scientists to implement custom models using advanced
ML algorithms
D. OCI AI services enable automatic tuning of hyperparameters during the training process
Answer: B
Explanation:
The primary advantage of using Oracle Cloud Infrastructure (OCI) AI services like OCI Vision or OCI
Language, compared to using the OCI Data Science service for machine learning applications, is that
OCI AI services offer pre-built, ready-to-use models for common AI tasks like image recognition and
language processing. This makes it easier and faster to implement AI solutions without the need for
extensive model development.
Question: 19
Which feature of Oracle Cloud Infrastructure's (OCI) AI services is most beneficial for developers who
want to quickly integrate AI capabilities into their applications without having to build models from
scratch?
A. OCI AI services provide out-of-the-box, pre-trained models for common use cases such as
language translation and image recognition.
B. OCI’s AI services are limited to a single region, restricting deployment flexibility across global data
centers.
C. Expert-level knowledge of machine learning required
D. OCI AI services focus on building machine learning pipelines rather than offering AI APIs.
Answer: A
Explanation:
The feature of Oracle Cloud Infrastructure's (OCI) AI services that is most beneficial for developers who
want to quickly integrate AI capabilities into their applications without having to build models from scratch
is that OCI AI services provide out-of-the-box, pre-trained models for common use cases such as
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language translation and image recognition. This allows for rapid implementation of AI functionalities
without extensive development efforts.
Question: 20
Which of the following best describes how Generative AI models like LLMs can enhance business
applications deployed on Oracle Cloud Infrastructure?
A. They provide a deterministic method for answering customer inquiries.
B. They eliminate the need for human oversight in AI-driven applications.
C. They enable real-time decision-making by learning from incoming data streams.
D. They allow for the automated generation of human-like content, such as reports or chat responses.
Answer: D
Explanation:
Generative AI models like LLMs enhance business applications by allowing for the automated generation
of human-like content, such as reports or chat responses. This capability helps improve customer
interactions and streamline content creation.
Question: 21
What is the primary role of the transformer architecture in Large Language Models (LLMs) like GPT
deployed in Oracle Cloud Infrastructure (OCI)?
A. The transformer architecture uses attention mechanisms to allow the model to consider
relationships between all tokens in a sequence, regardless of their position.
B. Transformers rely solely on convolutional layers to identify local patterns in input sequences, making
them ideal for image processing tasks.
C. The transformer architecture processes sequential data in a linear fashion, ensuring that the model
only focuses on the most recent token in the sequence.
D. The transformer architecture in LLMs applies recurrent layers to maintain long-term dependencies
across the input sequence.
Answer: A
Explanation:
The transformer architecture uses attention mechanisms to allow the model to consider relationships
between all tokens in a sequence, regardless of their position. This feature is essential for understanding
context and dependencies in text, making transformers highly effective for language modeling tasks.
Question: 22
Which of the following is a key difference between supervised and unsupervised learning in Machine
Learning (ML)?
A. In supervised learning, the model is trained using both input and output pairs, while in unsupervised
learning, the model is trained without any input data.
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B. Supervised learning uses labeled data, while unsupervised learning uses unlabeled data to predict
specific target variables.
C. Unsupervised learning is a subset of supervised learning used for data dimensionality reduction
tasks.
D. Supervised learning requires human-provided labels to guide the model, whereas unsupervised
learning identifies patterns in data without any predefined labels.
Answer: D
Explanation:
Supervised learning requires human-provided labels to guide the model, whereas unsupervised learning
identifies patterns in data without any predefined labels. This distinction is fundamental to understanding
the two approaches in machine learning.
Question: 23
Which two statements accurately explain how Long Short-Term Memory (LSTM) networks address the
limitations of traditional RNNs? (Select two)
A. LSTMs process sequences in reverse order to learn long-term dependencies.
B. LSTMs are designed to handle data with missing values more effectively than RNNs.
C. LSTMs contain a cell state that allows the network to carry forward important information across
long sequences.
D. LSTMs use a gating mechanism that helps them retain or forget information, addressing the
vanishing gradient problem.
E. LSTMs do not require as much computational power as RNNs, making them faster to train.
Answer: C, D
Explanation:
C: LSTMs contain a cell state that allows the network to carry forward important information across long
sequences. This mechanism helps in maintaining relevant information over longer contexts, addressing
one of the key limitations of traditional RNNs.
D: LSTMs use a gating mechanism that helps them retain or forget information, effectively addressing
the vanishing gradient problem, which is common in traditional RNNs. This ability enables LSTMs to
learn long-term dependencies more effectively.
Question: 24
Which of the following services are part of the Oracle Cloud Infrastructure (OCI) AI Portfolio? (Select
two)
A. OCI DevOps
B. OCI Vision
C. OCI Data Science
D. OCI Load Balancer
E. OCI Streaming
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Answer: B, C
Explanation:
OCI Vision and OCI Data Science are part of the Oracle Cloud Infrastructure (OCI) AI Portfolio. These
services provide capabilities for image analysis and machine learning model development, respectively.
Question: 25
You are a business analyst at a retail company, and your team wants to build a machine learning model
to forecast sales. However, your team lacks extensive data science expertise. You decide to explore
Oracle Cloud Infrastructure’s AutoML capabilities to automatically generate and train a machine learning
model. The data you have is organized in an Oracle Autonomous Data Warehouse, and you aim to get
predictions quickly to make business decisions. Which of the following strategies would best help you
leverage OCI’s AutoML capabilities to build and train a machine learning model for sales forecasting?
A. Use Oracle Analytics Cloud’s machine learning features to manually tune models, as it provides
more control over the training process.
B. Use Oracle Autonomous Data Warehouse’s built-in SQL for machine learning to train and deploy a
model without needing AutoML.
C. Use OCI Data Science AutoML features to automatically select the best model and
hyperparameters based on the sales data.
D. Train the model on OCI Data Science manually by writing custom code and using libraries like
TensorFlow or PyTorch, avoiding AutoML.
Answer: C
Explanation:
Utilizing OCI Data Science AutoML features allows the selection of the best model and hyperparameters
automatically based on the sales data, making it easier for non-experts to generate predictive models
quickly.
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