IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with optimizing a prompt-tuned large language model (LLM) using IBM Watsonx for a customer service chatbot. The chatbot needs to handle a variety of tasks, such as answering frequently asked questions (FAQs), providing detailed product descriptions, and troubleshooting user issues.
What is the most appropriate task to focus on during the initial tuning experiment?

A) Fine-tune the model to generate product descriptions using longer contextual prompts.
B) Focus on prompt-tuning the model for multi-turn dialogue to simulate more natural conversations.
C) Optimize the model for extractive question-answering from a predefined knowledge base.
D) Tune the model for text summarization, condensing user queries into shorter forms.

2. You are deploying a generative AI model for a financial services company. The model is responsible for automating customer support and providing recommendations. Due to the sensitive nature of financial data, the company emphasizes the need for robust AI governance.
What governance mechanism should you prioritize to ensure compliance with data privacy regulations and maintain trust in AI outputs?

A) Implementing role-based access control (RBAC) to restrict who can interact with the model.
B) Using AI explainability techniques to make the model's decisions transparent to regulators and customers.
C) Ensuring model version control to track changes and updates made to the model during the deployment process.
D) Regularly retraining the model to avoid performance degradation due to data drift.

3. You are developing an AI-driven application using IBM watsonx and LangChain to automate legal document summarization for a law firm. The application needs to extract key legal points, summarize them, and generate insights from various sources, including external APIs, court databases, and private document repositories. You are tasked with creating a LangChain chain that integrates these sources, customizes prompt templates, and uses Large Language Models (LLMs) to provide legal summaries. The prompt template must allow for dynamic insertion of text from external sources and adapt based on the type of legal document.
Which LangChain chain design would best meet the needs of this application?

A) Employ a Retrieval-Augmented Generation (RAG) Chain, where the LLM queries external knowledge sources in real-time while applying a fixed prompt template.
B) Design a ParallelChain where the text from different sources is processed in parallel by multiple LLMs, combining the results at the end.
C) Use a SequentialChain that first extracts text from external APIs and databases, processes it through custom prompt templates, and then sends the final processed text to an LLM.
D) Implement a SimpleChain that retrieves the required data from external APIs and directly sends the text to the LLM without prompt templates.

4. In the context of zero-shot prompting, you are developing a Watsonx AI model to generate a summary of financial reports. You input the following prompt: "Summarize the financial report in one sentence." Based on your understanding of zero-shot prompting, what outcome should you expect from the model?

A) The model will ask for additional data before generating the summary.
B) The model may generate a reasonable summary based solely on the prompt, even if it hasn't been explicitly trained on financial report summaries.
C) he model will refuse to generate a response due to lack of examples provided in the prompt.
D) The model will output a random sentence because zero-shot prompting is unreliable without examples.

5. A team is implementing a Retrieval-Augmented Generation (RAG) system for document search and retrieval. Their goal is to enable users to retrieve contextually relevant documents from a large, unstructured text corpus. They are considering using a vector database to handle this task.
In which scenario is a vector database the most appropriate choice for storing and retrieving documents?

A) When the system must support real-time updates and frequent data modifications, ensuring that query results always reflect the latest state of the data.
B) When exact match search is required, such as finding documents containing specific keywords or phrases.
C) When documents need to be retrieved based on semantic similarity to the user's query, even if the exact terms are not matched.
D) When all documents are structured and can be queried using traditional SQL queries, focusing on specific fields and categories.

Solutions: