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| Section | Weight | Objectives |
|---|---|---|
| OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Generative AI and Large Language Models | 15% | - Generative AI concepts
|
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NEW QUESTION # 43
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
Answer: A
Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
NEW QUESTION # 44
A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?
Answer: C
Explanation:
OCI Language provides pretrained natural language processing capabilities for analyzing unstructured text, including sentiment analysis. In this scenario, the requirement is specifically to determine whether customers express positive or negative opinions concerning individual aspects of a product or service. Oracle ' s OCI Language customer-feedback architecture describes extracting aspects from reviews and associating sentiments such as positive, negative, or neutral with those aspects. Oracle Docs Key phrase extraction identifies important topics or phrases, named entity recognition identifies entities such as organizations, people, or locations, and language detection determines the language in which content is written. None directly evaluates customer opinion. Sentiment analysis therefore precisely matches the business requirement because it transforms subjective review content into structured sentiment information that can be analyzed at scale.
NEW QUESTION # 45
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
Answer: C
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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NEW QUESTION # 46
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
Answer: C
Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.
NEW QUESTION # 47
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
Answer: D
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 # 48
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