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IBM C1000-185 Exam Syllabus Topics:

IBM watsonx Generative AI Engineer - Associate Sample Questions:

Question 1

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. Create longer, detailed prompts that cover all edge cases to reduce the need for multiple training iterations.
B. Use simple, concise instructions that avoid ambiguity but ensure all necessary constraints are included.
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.

Question 2

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 fine-tune the generative model, then store the embeddings in SingleStore, and use SQL queries to retrieve documents based on exact keyword matches.
B. 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.
C. Store the pre-trained generative model's parameters in SingleStore and use LangChain to retrieve embeddings from the database for training the model.
D. Use LangChain to generate embeddings and directly store the generated responses in SingleStore without any retrieval mechanism.

Question 3

While optimizing the cost of running a Generative AI model, you are instructed to adjust the prompt structure.
Which of the following changes to a prompt would most reduce computational costs while still maintaining effective results?

A. Breaking complex prompts into simpler, sequential prompts.
B. Including multiple tasks in a single prompt to maximize efficiency.
C. Using stop tokens early in the prompt to minimize generation length.
D. Switching from a narrative-style prompt to a bulleted list format.

Question 4

You are using InstructLab to fine-tune a large language model (LLM) for generating technical documentation. The model's output is inconsistent, sometimes too verbose and other times lacking critical details.
Which of the following actions within InstructLab will best help customize the model to consistently produce balanced, concise, yet informative outputs?

A. Increase the number of fine-tuning epochs to ensure the model converges
B. Adjust the token length limit in the model's configuration
C. Use prompt engineering to provide more explicit instructions for the model
D. Lower the batch size during fine-tuning to force the model to focus on smaller chunks of data

Question 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. Limit the training data to 100 samples of FAQs to prevent overfitting and keep the prompt-tuning process computationally efficient.
B. Utilize reinforcement learning to penalize long or irrelevant responses during the tuning phase, optimizing the model for concise output.
C. Introduce randomness in prompts by using variations in the wording for similar FAQs to improve the model's adaptability to different styles.
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: