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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.

Topic 5

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 6

Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

A. Computer Vision
B. Natural Language Processing
C. Anomaly Detection
D. Natural Language Processing

Answer: B

Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.

NEW QUESTION # 29
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They focus on increasing the number of tokens while keeping the model size constant.
B. They prioritize larger model sizes to achieve better performance.
C. They ensure that the model size, training time, and data size are balanced for optimal results.
D. They disregard model size and prioritize high-quality data only.

Answer: C

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.

NEW QUESTION # 30
What is the benefit of using embedding models in OCI Generative AI service?

A. They optimize the use of computational resources.
B. They enable creating detailed graphics.
C. They facilitate semantic searches.
D. They simplify managing databases.

Answer: C

Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for "financial reports," an embedding model can understand that "quarterly earnings" is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .

NEW QUESTION # 31
What distinguishes Generative AI from other types of AI?

A. Generative AI focuses on making decisions based on user interactions.
B. Generative AI uses algorithms to predict outcomes based on past data.
C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
D. Generative AI involves training models to perform tasks without human intervention.

Answer: C

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.

NEW QUESTION # 32
What are Convolutional Neural Networks (CNNs) primarily used for?

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

Answer: C

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 # 33
......

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