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1z0-1122-24 Reliable Oracle Cloud Infrastructure 2024 AI Foundations Associate Test Online - Free PDF Realistic Oracle 1z0-1122-24

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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.

Topic 2

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.

Topic 3

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 4

Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.

Topic 5

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 6

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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Explicability
B. Respect for human autonomy
C. Prevention of harm
D. Fairness

Answer: A

Explanation:
Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans. Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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Bottom of Form

NEW QUESTION # 39
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Text processing
B. Image generation
C. Time series prediction
D. Image classification

Answer: D

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.

NEW QUESTION # 40
What key objective does machine learning strive to achieve?

A. Improving computer hardware
B. Explicitly programming computers
C. Creating algorithms to solve complex problems
D. Enabling computers to learn and improve from experience

Answer: D

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.

NEW QUESTION # 41
What feature of OCI Data Science provides an interactive coding environment for building and training models?

A. Model catalog
B. Conda environment
C. Accelerated Data Science (ADS) SDK
D. Notebook sessions

Answer: D

Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.

NEW QUESTION # 42
What is the key feature of Recurrent Neural Networks (RNNs)?

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

Answer: B

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 # 43
......

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