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| Section | Weight | Objectives |
|---|---|---|
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Language - OCI Vision - OCI Document Understanding |
| Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Basics - Discuss AI Applications and Types of Data |
| Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals
|
| Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview - Explain Responsible AI |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
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NEW QUESTION # 22
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
Answer: A
Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.
NEW QUESTION # 23
What is the purpose of Attention Mechanism in Transformer architecture?
Answer: A
Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 24
What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
Answer: D
Explanation:
Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.
NEW QUESTION # 25
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
Answer: D
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 # 26
What is " in-context learning " in the realm of Large Language Models (LLMs)?
Answer: C
Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.
NEW QUESTION # 27
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