Free download Oracle Cloud Infrastructure 2024 AI Foundations Associate exam study material & Oracle 1z0-1122-24 instant download dumps

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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic	Details
Topic 1	Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
Topic 2	OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
Topic 3	Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.
Topic 4	Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q10-Q15):

NEW QUESTION # 10
What is the primary benefit of using the OCI Language service for text analysis?

A. It provides image processing capabilities.
B. It allows for text analysis at scale without machine learning expertise.
C. It only works with structured data.
D. It requires extensive machine learning expertise to use.

Answer: B

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

NEW QUESTION # 11
What does "fine-tuning" refer to in the context of OCI Generative AI service?

A. Doubling the neural network layers
B. Upgrading the hardware of the AI clusters
C. Encrypting the data for security reasons
D. Adjusting the model parameters to improve accuracy

Answer: D

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.

NEW QUESTION # 12
What is the key feature of Recurrent Neural Networks (RNNs)?

A. They process data in parallel.
B. They do not have an internal state.
C. They are primarily used for image recognition tasks.
D. They have a feedback loop that allows information to persist across different time steps.

Answer: D

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.

NEW QUESTION # 13
What is the primary purpose of reinforcement learning?

A. Identifying patterns in data
B. Finding relationships within data sets
C. Making predictions from labeled data
D. Learning from outcomes to make decisions

Answer: D

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.

NEW QUESTION # 14
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification and regression both predict continuous values.
B. Classification predicts continuous values, whereas regression assigns data points to categories.
C. Classification assigns data points to categories, whereas regression predicts continuous values.
D. Classification and regression both assign data points to categories.

Answer: C

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either "spam" or "not spam." On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers "which category?" regression answers "how much?" or "what value?".

NEW QUESTION # 15
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Free download Oracle Cloud Infrastructure 2024 AI Foundations Associate exam study material & Oracle 1z0-1122-24 instant download dumps

Oracle 1z0-1122-24 Exam Syllabus Topics:

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q10-Q15):

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