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IBM watsonx Generative AI Engineer - Associate Sample Questions:

1. You are building a generative AI model to assist with customer service responses. During evaluation, you notice that the responses generated tend to favor one specific demographic group, showing bias toward certain dialects and cultural references.
How should you adjust the prompt and model parameters to reduce this bias?

A) Incorporate additional training data from underrepresented demographic groups.
B) Use a prompt that explicitly asks for neutrality across demographic groups.
C) Switch to using deterministic (greedy) decoding to ensure more consistent outputs
D) Lower the temperature to reduce randomness in the model's response.

2. When crafting prompts for a generative AI model, readability is crucial to ensure clarity for both the model and human collaborators. You are asked to optimize the prompt to improve both the generation's accuracy and usability.
Which strategy would most effectively balance readability with optimal model performance?

A) Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.
B) Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.
C) Craft technical prompts that focus solely on model parameters, ignoring human readability for performance gains.
D) Focus on minimal prompts to reduce computational load, even if it sacrifices some clarity.

3. After conducting a prompt tuning experiment in IBM Watsonx, which two statistical metrics are most indicative of a model's ability to generalize well to unseen data? (Select two)

A) High training accuracy
B) Small difference between training and validation loss
C) Low training loss
D) Large difference between training and validation accuracy
E) Low validation loss

4. A team is using IBM InstructLab to customize a large language model (LLM) to automate responses in a healthcare chatbot application. The team wants to ensure the chatbot can handle user queries accurately, based on domain-specific instructions.
Which of the following correctly describes the role of the instruction optimization phase within the InstructLab workflow?

A) Instruction optimization refines prompts to improve the model's ability to follow task-specific instructions.
B) Instruction optimization focuses on improving the dataset's quality by removing outliers and noise.
C) Instruction optimization involves retraining the model on a larger dataset for better accuracy.

5. You are tasked with developing a customer support system for an e-commerce platform using the Retrieval-Augmented Generation (RAG) pattern. The system needs to retrieve relevant information from a large database of product specifications, user manuals, and FAQs. You decide to use LangChain for constructing the pipeline and SingleStore as the backend for storing and querying the document embeddings. The objective is to efficiently retrieve semantically similar documents and use them as input for a generative model that crafts human-like responses.
Which of the following steps best describes the correct implementation of the RAG pattern using LangChain and SingleStore for this customer support system?

A) Use LangChain to create an LLM chain that integrates with SingleStore for embedding retrieval, where the retrieved documents are used as context for generating responses.
B) Use LangChain to fine-tune the generative model, then store the embeddings in SingleStore, and use SQL queries to retrieve documents based on exact keyword matches.
C) Use LangChain to generate embeddings and directly store the generated responses in SingleStore without any retrieval mechanism.
D) Store the pre-trained generative model's parameters in SingleStore and use LangChain to retrieve embeddings from the database for training the model.

Solutions: