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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are tasked with implementing a Retrieval-Augmented Generation (RAG) pipeline for a customer service chatbot. Your goal is to ensure that the model can query a vast knowledge base, retrieve relevant documents, and generate responses that reference these documents. You are using the transformers and faiss libraries in Python to achieve this. After retrieving the relevant passages, the generative model will then process them to formulate a final response.
What is the most appropriate sequence of steps for implementing this RAG pipeline?

A) Encode the query using FAISS -> Use the transformer model to retrieve documents from the knowledge base -> Generate a response using the encoded documents.
B) Use the FAISS library to create an index -> Encode the input query using the transformer model -> Retrieve the most relevant documents -> Pass the retrieved documents to the generative model for response generation.
C) Generate the response first using the transformer model -> Retrieve documents from the knowledge base -> Encode the documents using FAISS -> Use the retrieved documents to fine-tune the generated response.
D) Initialize the generative model -> Use FAISS to encode the retrieved documents -> Pass the encoded documents to the transformer model for response generation.

2. In which scenario would using a soft prompt be more beneficial than a hard prompt in optimizing generative AI outputs?

A) When the model needs to generate a strictly factual output with minimal deviation from the prompt.
B) When the task requires explicit and consistent user instructions to ensure deterministic outcomes.
C) When fine-tuning a pre-trained model for domain-specific tasks, allowing the system to adapt its understanding through learned embeddings.
D) When the prompt needs to be manually adjusted by the user in real time during interaction with the AI.

3. You are tasked with fine-tuning a pre-trained large language model (LLM) for a customer service chatbot using IBM's InstructLab. You need to customize the LLM to improve its ability to handle specific user instructions related to order management, such as tracking orders, processing returns, and issuing refunds.
Which of the following components in InstructLab is the most critical for guiding the LLM to respond appropriately to these specific instructions?

A) The feedback loop system for real-time user input validation.
B) The prompt engineering interface for designing task-specific instructions.
C) The pre-processing pipeline for normalizing and standardizing the dataset.
D) The data loader module for transforming data into tokenized inputs.

4. You are designing a workflow using watsonx.ai to generate complex text summaries from multiple sources. To achieve this, you plan to implement a LangChain-based chain that orchestrates different generative AI tasks: document retrieval, natural language processing (NLP) analysis, and summarization.
What is the best way to structure the LangChain-based chain to ensure that each task is effectively handled and results in an accurate summary?

A) Break the LangChain-based chain into individual steps that allow for manual intervention at each stage, ensuring control over the process at every step.
B) Start with NLP analysis, pass the data to watsonx.ai for summarization, and then perform document retrieval to verify the accuracy of the summary.
C) Perform document retrieval first, followed by NLP analysis to extract relevant information, and then pass the processed data to watsonx.ai for summarization.
D) Use watsonx.ai to generate a summary immediately, and then perform NLP analysis and document retrieval in parallel to verify the accuracy of the output.

5. You are tasked with fine-tuning a pre-trained generative AI model for customer support automation. The goal is to enhance the model's performance in generating concise, relevant answers to frequently asked questions (FAQs). To do this, you need to optimize the prompt-tuning process.
Which two of the following techniques would be most effective for creating a prompt-tuned model for this purpose? (Select two)

A) Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
B) Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
C) Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
D) Shorten the prompts to the minimum number of words needed to address the FAQ directly, focusing on the key terms that drive the correct output.
E) Use a large set of domain-specific FAQs and fine-tune the model using those examples, ensuring that prompts are tailored to each type of question.

Solutions: