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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 2: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 3: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 4: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 5: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 6: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 7: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
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NEW QUESTION # 22
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?
Answer: D
Explanation:
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.
NEW QUESTION # 23
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
Answer: C
Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
NEW QUESTION # 24
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 # 25
Which statement describes the Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure Document Understanding?
Answer: C
Explanation:
The Optical Character Recognition (OCR) feature of Oracle Cloud Infrastructure (OCI) Document Understanding recognizes and extracts text from documents. This capability is fundamental for converting printed or handwritten text into a machine-readable format, allowing for further processing, such as text analysis, search, and archiving. OCI ' s OCR is an essential tool in automating document processing workflows, enabling businesses to digitize and manage their documents efficiently.
NEW QUESTION # 26
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
Answer: C
Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .
NEW QUESTION # 27
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