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| Section | Objectives |
|---|---|
| Topic 1: Retrieval-Augmented Generation (RAG) | - Document ingestion and retrieval pipelines - Grounding and hallucination mitigation - Vector databases and embeddings |
| Topic 2: Foundations of Generative AI | - Transformer architecture overview - Tokenization and embeddings - Large Language Models (LLMs) fundamentals |
| Topic 3: IBM watsonx.ai and Platform Capabilities | - Prompt Lab usage and tooling - watsonx.ai core features - Model selection and deployment workflows |
| Topic 4: Model Evaluation and Governance | - Evaluation metrics for LLMs - Model monitoring and lifecycle management - Bias, fairness, and responsible AI |
| Topic 5: Prompt Engineering | - Few-shot and zero-shot prompting - Prompt tuning and optimization strategies - Prompt design techniques |
1. In a system that generates product recommendations using a generative AI model, you are tasked with creating prompts that incorporate dynamic variables to ensure more relevant and personalized responses.
Prompt Template: "Recommend products for [customer_name] based on their interest in [product_category]." Which portion of the prompt is the best candidate to be replaced with variables to achieve greater personalization and flexibility? (Select two)
A) "Recommend products"
B) "[product_category]"
C) "[customer_name]"
D) "based on their interest"
E) "for"
2. You are tasked with creating a prompt-tuned model that generates optimal, task-specific responses for a financial advisory chatbot. Your goal is to improve the model's accuracy in answering financial queries, and you need to determine the right parameters to focus on during the tuning process.
Which two of the following strategies are most effective in optimizing prompt-tuned models for accuracy? (Select two)
A) Apply a low temperature setting (e.g., 0.2) during inference to ensure more deterministic and precise responses.
B) Use a beam search decoding algorithm with a large beam width to generate a variety of response candidates for each query.
C) Increase the number of layers fine-tuned in the model to capture deeper contextual information from financial data.
D) Choose an initial learning rate that is high to encourage faster convergence during the fine-tuning process.
E) Include domain-specific financial terms in the prompt-tuning data to help the model specialize in accurate financial advice generation.
3. You are building a generative AI system that uses synthetic data to mimic an existing dataset. You have learned about two primary algorithms: one that focuses on ensuring the synthetic data passes statistical normality tests and another designed to generate realistic-looking data without focusing on distribution conformity.
Which algorithm should you choose if your primary concern is statistical accuracy and passing the Anderson-Darling test?
A) K-Nearest Neighbors (KNN)
B) Gaussian Mixture Models (GMMs)
C) Anderson-Darling Based Synthetic Data Generation (ADS-DG)
D) Bootstrapping Algorithm
4. A developer is using a GitHub Code Retrieval API to help build a search engine that can locate relevant code snippets from public repositories. The API is designed to retrieve code based on the semantic similarity of the query (e.g., a description of what the code does) to the code itself.
What is the primary advantage of using a vector-based approach for code retrieval in this scenario?
A) It speeds up retrieval by limiting the search to repositories where the user has committed code in the past.
B) It retrieves code snippets based on the semantic similarity between the query and the code, enabling the discovery of relevant code even when the query and code do not use the same keywords.
C) It ensures that the code snippets returned are exact matches to the keywords in the query, avoiding irrelevant code.
D) It provides real-time updates to the code embeddings, ensuring the latest code is always retrieved.
5. Which of the following best describes the benefit of using prompt variables when developing generative AI models in IBM Watsonx?
A) Prompt variables ensure that the same text input will always yield the same output, improving consistency.
B) Prompt variables allow for dynamic input customization without needing to manually modify the core prompt, increasing flexibility.
C) Using prompt variables in Watsonx allows the model to learn from real-time data input, enabling self-training over time.
D) Prompt variables reduce computational load by optimizing model performance, improving overall system efficiency.
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: A,E | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: B |
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