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| Section | Objectives |
|---|---|
| Foundations of Generative AI | - Tokenization and embeddings - Transformer architecture overview - Large Language Models (LLMs) fundamentals |
| IBM watsonx.ai and Platform Capabilities | - watsonx.ai core features - Prompt Lab usage and tooling - Model selection and deployment workflows |
| Prompt Engineering | - Few-shot and zero-shot prompting - Prompt tuning and optimization strategies - Prompt design techniques |
| Retrieval-Augmented Generation (RAG) | - Vector databases and embeddings - Document ingestion and retrieval pipelines - Grounding and hallucination mitigation |
| Model Evaluation and Governance | - Evaluation metrics for LLMs - Model monitoring and lifecycle management - Bias, fairness, and responsible AI |
You want a generative AI model to summarize a lengthy text in one sentence. You provide the following prompt: "Summarize the following paragraph in one sentence: 'Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn. The field of AI includes everything from speech recognition to problem-solving and robotics.'" No prior examples are given.
What type of prompting is being used, and what are the expectations?
Correct Answer: B 🗳️
You are implementing a RAG system that uses vector embeddings to retrieve relevant information from a large corpus of documents. To store and efficiently query the vector embeddings, you need to choose a suitable vector database.
Which of the following is a key capability that a vector database must support to ensure efficient retrieval in this context?
Correct Answer: A 🗳️
While preparing a fine-tuning project using IBM watsonx, you want to generate synthetic data via the User Interface (UI) to supplement your existing dataset.
Which of the following describes an option supported by IBM watsonx for synthetic data generation through the UI?
Correct Answer: B 🗳️
When optimizing the tuning process in IBM watsonx Tuning Studio, what is the main purpose of fine-tuning metering options?
Correct Answer: A 🗳️
You are building a chatbot using a generative AI model for a medical advice platform. During testing, you notice that the model occasionally generates medical information that contradicts established guidelines. This is an example of a model hallucination.
Which prompt engineering technique would best mitigate the risk of hallucination in this scenario?
Correct Answer: C 🗳️
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