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Oracle 1z0-1122-24 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.
Topic 4	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 5	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 6	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.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which capability is supported by Oracle Cloud Infrastructure Language service?

A. Analyzing text to extract structured information like sentiment or entities
B. Converting text into images
C. Translating text into speech
D. Detecting objects and scenes in images

Answer: A

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.

NEW QUESTION # 38
In machine learning, what does the term "model training" mean?

A. Establishing a relationship between input features and output
B. Analyzing the accuracy of a trained model
C. Performing data analysis on collected and labeled data
D. Writing code for the entire program

Answer: A

Explanation:
In machine learning, "model training" refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

NEW QUESTION # 39
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

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

Answer: D

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

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

Answer: B

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 is the key feature of Recurrent Neural Networks (RNNs)?

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

Answer: A

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 # 42
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