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| Section | Weight | Objectives |
|---|---|---|
| Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
- Discuss Unsupervised Learning Fundamentals |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
| Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview |
| Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview - Explain Responsible AI - Discuss OCI ML Services Overview |
| Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Language - OCI Vision - OCI Speech |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services |
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NEW QUESTION # 29
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: B
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 # 30
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
Answer: C
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 # 31
What is the key feature of Recurrent Neural Networks (RNNs)?
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 # 32
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
Answer: C
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 # 33
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
Answer: A
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 # 34
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