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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
| Topic 2: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search |
| Topic 3: Intro to Generative AI and LLMs | 15% | - Explain Transformers Fundamentals - Discuss Generative AI Overview - Explain LLM Fine Tuning - Discuss Large Language Models Fundamentals - Explain Prompt Engineering and Instruction Tuning |
| Topic 4: Intro to ML Foundations | 15% | - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
- Discuss Reinforcement Learning Fundamentals |
| Topic 5: Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview |
| Topic 6: Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Language - OCI Vision - OCI Speech |
| Topic 7: Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) |
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NEW QUESTION # 14
Which AI domain can be employed for identifying patterns in images and extract relevant features?
Answer: C
Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.
NEW QUESTION # 15
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 # 16
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 # 17
How does AI enhance human efforts?
Answer: A
Explanation:
AI enhances human efforts by processing large volumes of data quickly and accurately, performing complex computations that would be time-consuming or impossible for humans to handle manually. This allows humans to focus on more strategic, creative, and decision-making tasks, leveraging AI ' s ability to provide insights, automate repetitive processes, and support decision-making. AI does not physically enhance human capabilities, nor does it replace human workers in all tasks. Instead, it serves as an augmentation tool, amplifying human productivity and capabilities.
NEW QUESTION # 18
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
Answer: C
Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
* Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
* Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.
NEW QUESTION # 19
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